- Cyber Security, Grid Computing, Cloud Computing, Machine Learning, Network Security, Fault Tolerant Computing, and 22 moreFault Detection, Fault Tolerant Control, Fault Tolerance In Cloud Computing, Resource Allocation, Optimization techniques, Particle Swarm Optimization, Meta-heuristics, Heuristic and Meta-heuristic Algorithms, Meta-Heuristics (Tabu Search, Genetic Algorithms, Simulated Annealing, Ant-Colony, Particle Swarm Optimization), Optimization Techniques: Genetic Algorithm; Differential Evolution, Human Resource Management, Strategic Human Resource Management, Scheduling, Mobile Cloud Computing, Cloud, Cloud Computing Security, Cloud Security, Security in Cloud Computing, Task scheduling algorithms in Cloud computing systems, Job Scheduling on Grid Computing and Cloud Computing, Web Mining and Cloud Computing, and Cloud Computing and Virtualizationedit
- Shafi’i Muhammad ABDULHAMID received his PhD in Computer Science from Universiti Teknologi Malaysia (UTM), MSc in Com... moreShafi’i Muhammad ABDULHAMID received his PhD in Computer Science from Universiti Teknologi Malaysia (UTM), MSc in Computer Science from Bayero University Kano (BUK), Nigeria and a Bachelor of Technology in Mathematics/Computer Science from the Federal University of Technology Minna, Nigeria. His current research interests are in Cyber Security, Cloud computing, Soft Computing and BigData. He has published many academic papers in reputable International journals, conference proceedings and book chapters. He has been appointed as an Editorial board member for UPI JCSIT and IJTRD. He has also been appointed as a reviewer of several ISI and Scopus indexed International journals such as JNCA Elsevier, EIJ Elsevier, JKSU-CIS Elsevier, IJNS, IJST, IJCT, JITE:Research, JITE:IIP, JAIT, IJAER and JCEIT SciTechnol. He is a member of IEEE, International Association of Computer Science and Information Technology (IACSIT), Computer Professionals Registration Council of Nigeria (CPN), International Association of Engineers (IAENG), The Internet Society (ISOC), Cyber Security Experts Association of Nigeria (CSEAN) and Nigerian Computer Society (NCS). Presently he is a lecturer at the Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.edit
- Prof. Dr. Muhammad Shafie Abd Latiff (UTM Malaysia)edit
Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false... more
Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was...
Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique-of humpback whales-for solving the complex optimization problems. It has been... more
Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique-of humpback whales-for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors...
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Fog computing is a new paradigm of computing that extends cloud-computing operations to the edges of the network. The fog-computing services provide location sensitivity, reduced latency, geographical accessibility, wireless connectivity,... more
Fog computing is a new paradigm of computing that extends cloud-computing operations to the edges of the network. The fog-computing services provide location sensitivity, reduced latency, geographical accessibility, wireless connectivity, and enhanced improved data streaming. However, this computing paradigm is not an alternative for cloud computing and it comes with numerous security and privacy challenges. This paper provides a systematic literature review on the security challenges in fog-computing system. It reviews several architectures that are vital to support the security of fog environment and then created a taxonomy based on the different security techniques used. These include machine learning, cryptographic techniques, computational intelligence, and other techniques that differentiate this paper from the previous reviews in this area of research. Nonetheless, most of the proposed techniques used to solve security issues in fog computing could not completely addressed the security challenges due to the limitation of the various techniques. This review is intended to guide experts and novice researchers to identify certain areas of security challenges in fog computing for future improvements.
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Cloud computing is defined as the delivery of on-demand computing resources ranging from infrastructure, application to datacenter over the internet on a pay-per-use basis. Most cloud computing applications does not guarantee high level... more
Cloud computing is defined as the delivery of on-demand computing resources ranging from infrastructure, application to datacenter over the internet on a pay-per-use basis. Most cloud computing applications does not guarantee high level of security such as privacy, confidentiality and integrity of data because of third-party transition. This brings the development of Blowfish cloud encryption system that enables them to encrypt their data before storage in the cloud. Blowfish encryption scheme is a symmetric block cipher used to encrypt and decrypt data. Microsoft Azure cloud server was used to test the proposed encryption system. Users are able to encrypt their data and obtain a unique identification to help them retrieve encrypted data from the cloud storage facility as when needed.
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Dynamic topology change and decentralized makes routing a challenging task in mobile ad hoc network. Energy efficient routing is the most challenging task in MANET due to limited energy of mobile nodes. Limited power of batteries... more
Dynamic topology change and decentralized makes routing a challenging task in mobile ad hoc network. Energy efficient routing is the most challenging task in MANET due to limited energy of mobile nodes. Limited power of batteries typically use in MANET, and this is not easy to change or replace while running communication. Network disorder can occur for many factors but in middle of these factors deficiency of energy is the most significant one for causing broken links and early partition of the network. Evenly distribution of power between nodes could enhance the lifetime of the network, which leads to improving overall network transmission and minimizes the connection request. To discourse this issue, we propose an Energy Aware Routing Protocol (EARP) which considers node energy in route searching process and chooses nodes with higher energy levels. The EARP aim is to establish the shortest route from source to destination that contains energy efficient nodes. The performance of E...
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Spamming has attained a global dimension and continued to maintain an upward trend, both in sophistication and frequency. So far, it has defied every effort, including technical and non-technical proposals, to curb it. This study seeks to... more
Spamming has attained a global dimension and continued to maintain an upward trend, both in sophistication and frequency. So far, it has defied every effort, including technical and non-technical proposals, to curb it. This study seeks to investigate the prevalence of spam SMS, with focus on Nigeria. To quantify the prevalence, primary data was collected using questionnaire. Out of 270 surveyed, the responses of 191 mobile users were valid and analyzed. The study revealed that all mobile subscribers receive spam SMS, receiving an average of 2.45 spam SMS daily. This implies an average of 334,857,685 spam SMS received daily in Nigeria. However, most are for commercial purposes. Few mobile users report cases of fraudulent spam SMS, including those with SMShing intent, to network providers or security agencies. Most believe customers of mobile networks should reserve the right to determine the type of unsolicited SMS to be received, and unsolicited advertorial/promotional SMS should be regulated. Current guidelines and regulations need to be reviewed, to effectively manage spamming activities in Nigeria.
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Online social networks are becoming a major growth point of the internet, as individuals, companies and governments constantly desire to interact with one another, the ability of the internet to deliver this networking capabilities grows... more
Online social networks are becoming a major growth point of the internet, as individuals, companies and governments constantly desire to interact with one another, the ability of the internet to deliver this networking capabilities grows stronger. In this paper, we looked at the structure and components of the member profile and the challenges of privacy issues faced by individuals and governments that participate in social networking. We also looked at how it can be used to distort national security, how it became the new weapons of mass mobilization and also how social networks have became the rallying forces for revolutions and social justice.
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Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. In this paper... more
Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. In this paper , we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future.
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The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter... more
The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.
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Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic... more
Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.
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Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques... more
Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques which includes misclassification of genuine emails as spam (false positives) are a growing challenge to the internet world. This research work proposed the use of a metaheuristic optimization algorithm, the whale optimization algorithm (WOA), for the selection of salient features in the email corpus and rotation forest algorithm for classifying emails as spam and non-spam. The entire datasets were used, and the evaluation of the rotation forest algorithm was done before and after feature selection with WOA. The results obtained showed that the rotation forest algorithm after feature selection with WOA was able to classify the emails into spam and non-spam with a performance accuracy of 99.9% and a low FP rate of 0.0019. This shows that the proposed method had produced a remarkable improvement as compared with some previous methods.
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In this paper research was carried out in order to evaluate the security risk analysis and management in banking company through the use of a questionnaire to determine the level of risk that customer of the financial institution is... more
In this paper research was carried out in order to evaluate the security risk analysis and management in banking company through the use of a questionnaire to determine the level of risk that customer of the financial institution is likely to encounter. It was discovered that though the majority of financial institution users are familiar with the possible risk associated with some banking transaction, some aspect still exists that financial institution users are not familiar with which serves as a vulnerable point that could be exploited. The study makes a recommendation for proper enlightenment of financial institution users so as to stay abreast with possible security challenge associated with some banking transaction processes to be able to mitigate possible exploit.
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In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of... more
In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.
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—Mobile wallet is a payment platform that stores money as a value in a digital account on mobile device which can then be used for payments with or without the need for the use credit/debit cards. The cases of cyber-attacks are on the... more
—Mobile wallet is a payment platform that stores money as a value in a digital account on mobile device which can then be used for payments with or without the need for the use credit/debit cards. The cases of cyber-attacks are on the rise, posing threats to the confidentiality, integrity and availability of information systems including the mobile wallet transactions. Due to the adverse impacts of cyber-attacks on the mobile payment service providers and the users, as well as the risks associated with the use of information systems, performing risk management becomes imperative for business organizations. This research work focuses on the assessment of the vulnerabilities associated with mobile wallet transactions and performs an empirical risk management in order to derive the security priority level needed to ensure the security and privacy of the users of mobile wallet platforms. Based on the extensive literature review, a structured questionnaire was designed and administered to the mobile wallet users who are Paga student customers via the internet. A total number of 52 respondents participated in the research and their responses were analyzed using descriptive statistics. The results of the analysis show that mobile wallet Login details are the most important part of customer information that need to be highly protected as their compromise is likely to affect others. Also, customers' information such as Mobile Wallet Account Number, Registered Phone Number, Linked ATM Card details, and Linked ATM Card PIN among others are also plausible to attacks. Hence, different security priority levels were derived to safeguard each of the components and possible security tools and mechanisms are recommended. The study also revealed that there are vulnerabilities from the mobile wallet users end that also pose threat to the security of the payment system and customers' transaction which need to be properly addressed. This research work will enable the mobile payment service providers focus on their services and prioritize the security solutions for each user's information types or components base on the risks associated with their system and help in taking an inform security related decisions.
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—Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. Concerning that, the key concern is to map the virtual machines (VMs) with... more
—Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. Concerning that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Since scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using a novel metaheuristic Whale optimization algorithm (WOA). Furthermore, we present the problem formulation for the framework to achieve multi-objective functions.
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Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of " big data ". ANN are known for their effectiveness and efficiency for small datasets, and this era of big data has... more
Approximately 2.5 quintillion bytes of data are emitted on a daily basis, and this has brought the world into the era of " big data ". ANN are known for their effectiveness and efficiency for small datasets, and this era of big data has posed a challenge to the big data analytics using ANN. Recently, much research effort has been devoted to the application of ANN in big data analytics and is still ongoing, although it is in it is early stages. The purpose of this paper is to summarise recent progress, challenges and opportunities for future research. This paper presents a concise view of the state-of-the-art, challenges and future research opportunities regarding the applications of ANN in big data analytics, and reveals that progress has been made in this area. Our review points out the limitations of the previous approaches, the challenges in the ANN approaches in terms of their applications in big data analytics, several ANN architecture that have not yet been explored in big data analytics and opportunities for future research. We believe that this paper can serve as a yardstick for future progress on the applications of ANN in big data analytics as well as a starting point for new researchers with an interest in the exploration of ANN in big data analytics.
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Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are... more
Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are significantly different from the procedure or techniques used for single-objective optimizations. For this purpose, meta-heuristic algorithms always show their strength to deal with multi-objective optimization problems. In this research article, we present an innovative Multi-objective Cuckoo Search Optimization (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The main objective of resource scheduling problem is to reduce the cloud user cost and enhance the performance by minimizing makespan time, which helps to increase the revenue or profit for cloud providers with maximum resource utilization. Therefore, the proposed MOCSO algorithm is a new method for solving multi-objective resource scheduling problems in IaaS cloud computing environment. Moreover, the effects of the proposed algorithm are analyzed and evaluated by comparison with state-of-the-art multi-objective resource scheduling algorithms using simulation framework. Results obtained from simulation show that the proposed MOSCO algorithm performs better than MOACO, MOGA, MOMM and MOPSO, and balance multiple objectives in terms of expected time to completion and expected cost to completion matrices for resource scheduling in IaaS cloud computing environment.
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Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard... more
Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis.
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—As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing... more
—As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing tools have become easily available on the internet, which has made people who are a novice in the field of image editing, to be capable of tampering with an image easily without leaving any visible clue or trace behind, which has led to increase in digital images losing authenticity. This has led to developing various techniques for tackling authenticity and integrity of forged images. In this paper, a robust and enhanced algorithm is been developed in detecting copy-move forgery, which is done by hybridizing block-based DCT (Discrete Cosine Transform) technique and a keypoint-based SURF (Speeded-Up Robust Feature)technique using the MATLAB platform. The performance of the above technique has been compared with DCT and SURF techniques as well as other hybridized techniques in terms of precision, recall, FPR and accuracy metrics using MICC-F220 dataset. This technique works by applying DCT to the forged image, with the main goal of enhancing the detection rate of such image and then SURF is applied to the resulting image with the main goal of detecting those areas that are been tampered with on the image. It has been observed that this paper's technique named HDS has an effective detection rate on the MICC-F220 dataset with multiple cloning attacks and other various attacks such as rotation, scaling, a combination of scaling plus rotation, blur, compression, and noise. Index Terms—Copy-move image forgery, block-based method, keypoint-based method, DCT, SURF
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The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called... more
The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Naï ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Naï ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms' performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.
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One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down... more
One of the dangers faced by various organizations and institutions operating in the cyberspace is Distributed Denial of Service (DDoS) attacks; it is carried out through the internet. It resultant consequences are that it slow down internet services, makes it unavailable, and sometime destroy the systems. Most of the services it affects are online applications and procedures, system and network performance, emails and other system resources. The aim of this work is to detect and classify DDoS attack traffics and normal traffics using multi layered feed forward (FFANN) technique as a tool to develop model. The input parameters used for training the model are: service count, duration, protocol bit, destination byte, and source byte, while the output parameters are DDoS attack traffic or normal traffic. KDD99 dataset was used for the experiment. After the experiment the following results were gotten, 100% precision, 100% specificity rate, 100% classified rate, 99.97% sensitivity. The detection rate is 99.98%, error rate is 0.0179%, and inconclusive rate is 0%. The results above showed that the accuracy rate of the model in detecting DDoS attack is high when compared with that of the related works which recorded detection accuracy as 98%, sensitivity 96%, specificity 100% and precision 100%.
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Ministries, Department and Agencies (MDA " s) websites are useful constituents for information dissemination and citizen centric services. Various vulnerabilities exist in this websites. In this paper, vulnerabilities found in MDA " s... more
Ministries, Department and Agencies (MDA " s) websites are useful constituents for information dissemination and citizen centric services. Various vulnerabilities exist in this websites. In this paper, vulnerabilities found in MDA " s website are categorized and analyzed based on Open Web Application Security Project (OWASP) Top 10 to understand impact of these vulnerabilities on web security of MDA " s websites. In this study we have analyzed security pertaining to 10 MDA " s websites. We found vulnerabilities in all websites with different degree of security risk. To achieve the results we have cross tabulated vulnerabilities found in these websites with their security risk level. As a result the research work found that vulnerability A4-insecure direct object reference with 49% is the main contributor of web security risk in MDA " s websites. Apart from this it is clearly evident that majority of the vulnerabilities found in MDA " s websites belongs to informational risk group with 45.82% but still few high impacting vulnerabilities exists and needs to be handle without delay. Thus, the paper contributed towards the understanding of web security risk in MDA " s websites.
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In recent past, there are a lot of research advancements in mobile forensics tools. This is so due to increase usage of mobile phones in storage of information, law enforcement, mobile online transactions, and also negatively by criminals... more
In recent past, there are a lot of research advancements in mobile forensics tools. This is so due to increase usage of mobile phones in storage of information, law enforcement, mobile online transactions, and also negatively by criminals due to increased computational capabilities. Mobile forensics devices continue to remain a very challenging task due to poor user data retrieval techniques for evidence retrieval. Recently, third party applications assume a veritable feet because it is supported by majority of mobile devices platforms, thereby making it easy to extract information of its users' for future criminal audit. This paper proposes an evidence data retrieval method from InstagramApp using two networks based platforms (that is, pure peer-to-peer (PPP) and special cluster peer (SCP) based), whose concept is to manage mobile device communication and generate multiple copies of users data/information to be dumped across three servers. The forensic test results were obtained from PPP and SCP developed to securely extract data from mobile devices. This shows that, SCP outperformed PPP in terms of the time taken to fulfil forensic auditor's request, throughput and broadband utilisation which are 42.82% to 57.18%, 56.81% to 43.19% and 35.41% to 64.53% respectively.
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The impact of forensic evidence found on smartphones cannot be overemphasized when compared to that found on their digital counterparts such as personal computers (PCs). Recently, third-party instant messaging applications have gradually... more
The impact of forensic evidence found on smartphones cannot be overemphasized when compared to that found on their digital counterparts such as personal computers (PCs). Recently, third-party instant messaging applications have gradually replaced the traditional messaging applications and as such they contain a large amount of information which are far from forensic solutions. This seminar paper focuses on the forensic analysis of Kik messenger: a multi-platform instant messaging or chat application on android devices. Forensic images of three android devices with android versions 4.4 (KitKat), and 5.0 (Lollipop) and different android manufacturers are captured and data related to Kik are identified and examined. Artefacts of forensic values are then identified and analyzed. The result of this research will help digital forensic investigators and academia alike in locating and acquiring digital evidence from Kik messenger on android platforms.
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The rapid rate of technology advancement cannot be left unnoticed especially when it comes to the ICT sector. Ironically, this has also brought about an increase in cybercrime worldwide, thus forensic agencies and analysts are constantly... more
The rapid rate of technology advancement cannot be left unnoticed especially when it comes to the ICT sector. Ironically, this has also brought about an increase in cybercrime worldwide, thus forensic agencies and analysts are constantly on the move to investigate and acquire evidences at various crime scenes. Amongst all digital devices relating to forensic analysis, mobile phones are one of the most troublesome. Acquiring, decoding and presenting information resident in mobile device is a complex and challenging process. Several tools and methods both commercial and open source have been and are being developed to ensure authenticity and integrity in mobile forensic investigation and evidence acquisition. However, the level of result accuracy of these tools based on the mobile platform must be understood before even employing them in an investigation process. This paper examines the Android and Windows phone platform comparing the accuracy level of information extracted.
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Mobile phone devices have become popular among every age and social grouping in every society and are utilised by lots of people for different purposes. As the design of Mobile phones are continually evolving due to advancement in present... more
Mobile phone devices have become popular among every age and social grouping in every society and are utilised by lots of people for different purposes. As the design of Mobile phones are continually evolving due to advancement in present technologies, applications that run on them are also being updated to fully utilise features on new devices. Due to the flexibility and portability coupled with applications that make communication easy and accessible, these devices are now mostly used to perform e-transactions, social networking and even criminal activities. One of such applications is WhatsApp which over various versions have tried to maintain the confidentiality and integrity of messages sent and received using WhatsApp. Securing of data from Criminals or unauthorized users called for constant updating of the encryption scheme of the SQLite database which is usually saved on the memory of the device on which it is installed. Over many updates of WhatsApp, the encryption has been changed from db.crypt, db.crypt5, db.crypt7, db.crypt8 to db.crypt12. There is need for forensic expert to constantly update their knowledge so as to get the needed information from the database. This study presents a forensic process of extracting WhatsApp data from db.crypt12, which is the latest SQLite Database encryption used by WhatsApp to secure stored communication data. The steps involve using some open source tools that can be downloaded for free on the internet.
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A particularly detrimental attack ravaging campus and ad hoc networks is the Sybil attack, where a node illegally claims more than one identity, targeting honest and genuine nodes, thereby weaken or disrupting the system. This research... more
A particularly detrimental attack ravaging campus and ad hoc networks is the Sybil attack, where a node illegally claims more than one identity, targeting honest and genuine nodes, thereby weaken or disrupting the system. This research work aimed at designing a mitigation mechanism to combat the attack on a Campus network (CANET). Setting threshold and checking for IP address spoofing by comparing nodes physical addresses is the focus of this work. The success of the algorithm is validated by simulation, and quality of service is guaranteed with regards to the detection rate and packet delivery ratio.
Research Interests:
In its simplest structure, cloud computing technology is a massive collection of connected servers residing in a datacenter and continuously changing to provide services to users on-demand through a front-end interface. The failure of... more
In its simplest structure, cloud computing technology is a massive collection of connected servers residing in a datacenter and continuously changing to provide services to users on-demand through a front-end interface. The failure of task during execution is no more an accident but a frequent attribute of scheduling systems in a large-scale distributed environment. Recently, some computational intelligence techniques have been mostly utilized to decipher the problems of scheduling in the cloud environment, but only a few emphasis on the issue of fault tolerance. This research paper puts forward a Checkpointed League Championship Algorithm (CPLCA) scheduling scheme to be used in the cloud computing system. It is a fault-tolerance aware task scheduling mechanisms using the checkpointing strategy in addition to tasks migration against unexpected independent task execution failure. The simulation results show that, the proposed CPLCA scheme produces an improvement of 41%, 33% and 23% as compared with the Ant Colony Optimization (ACO), Genetic Algorithm (GA) and the basic league championship algorithm (LCA) respectively as parametrically measured using the total average makespan of the schemes. Considering the total average response time of the schemes, the CPLCA scheme produces an improvement of 54%, 57% and 30% as compared with ACO, GA and LCA respectively. It also turns out significant failure decrease in jobs execution as measured in terms of failure metrics and performance improvement rate. From the results obtained, CPLCA provides an improvement in both tasks scheduling performance and provides failure awareness that is more appropriate for scheduling in the cloud computing model.
Research Interests:
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of... more
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
Research Interests:
Aim/Purpose Electronic examinations have some inherent problems. Students have expressed negative opinions about electronic examinations (e-examinations) due to a fear of, or unfamiliarity with, the technology of assessment, and a lack of... more
Aim/Purpose Electronic examinations have some inherent problems. Students have expressed negative opinions about electronic examinations (e-examinations) due to a fear of, or unfamiliarity with, the technology of assessment, and a lack of knowledge about the methods of e-examinations. Background Electronic examinations are now a viable alternative method of assessing student learning. They provide freedom of choice, in terms of the location of the examination, and can provide immediate feedback; students and institutions can be assured of the integrity of knowledge testing. This in turn motivates students to strive for deeper learning and better results, in a higher quality and more rigorous educational process. Methodology This paper compares an e-examination system at FUT Minna Nigeria with one in Australia, at the University of Tasmania, using case study analysis. The functions supported, or inhibited, by each of the two e-examination systems, with different approaches to question types, cohort size, technology used, and security features, are compared. Contribution The researchers' aim is to assist stakeholders (including lecturers, invigilators, candidates, computer instructors, and server operators) to identify ways of improving the process. The relative convenience for students, administrators, and lecturer/assessors and the reliability and security of the two systems are considered. Challenges in conducting e-examinations in both countries are revealed by juxtaposing the systems. The authors propose ways of developing more effective e-examination systems.
Research Interests:
Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries... more
Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges and future research directions on spam detection techniques, filtering and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analysed. The most popular techniques for SMS spam detection, filtering and mitigation are compared, including the used datasets, their findings and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement.
Research Interests:
Effective resource scheduling is essential for the overall performance of cloud computing system. Resource scheduling problem in IaaS cloud computing is investigated in this paper. It is established to be an NP-hard problem. A recently... more
Effective resource scheduling is essential for the overall performance of cloud computing system. Resource scheduling problem in IaaS cloud computing is investigated in this paper. It is established to be an NP-hard problem. A recently developed Cuckoo Search (CS) meta-heuristic algorithm is proposed in this paper, to minimize the response time, makespan and throughput for the resource scheduling in IaaS cloud computing. Simulation results show that CS algorithm outperforms that of Ant Colony Optimization (ACO) algorithm based on the considered parameters.
Research Interests:
There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a... more
There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.
Research Interests:
In cloud computing, for the effective performance of any system, there is a need of effective resource scheduling. A resource scheduling problem in IaaS cloud computing is considered in this paper. Resource scheduling problem is proved to... more
In cloud computing, for the effective performance of any system, there is a need of
effective resource scheduling. A resource scheduling problem in IaaS cloud computing is
considered in this paper. Resource scheduling problem is proved to be NP-hard. A recently
developed cuckoo search (CS) meta-heuristic algorithm is presented in this paper, to
minimize the execution time, makespan and throughput for the resource scheduling in IaaS
cloud computing. Simulation results show that CS algorithm outperforms many other metaheuristic
algorithms.
effective resource scheduling. A resource scheduling problem in IaaS cloud computing is
considered in this paper. Resource scheduling problem is proved to be NP-hard. A recently
developed cuckoo search (CS) meta-heuristic algorithm is presented in this paper, to
minimize the execution time, makespan and throughput for the resource scheduling in IaaS
cloud computing. Simulation results show that CS algorithm outperforms many other metaheuristic
algorithms.
Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment... more
Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.
Research Interests: Scheduling, Cloud Computing, Mobile Cloud Computing, Cloud, Cloud Computing Security, and 6 moreCloud Security, Security in Cloud Computing, Task scheduling algorithms in Cloud computing systems, Job Scheduling on Grid Computing and Cloud Computing, Web Mining and Cloud Computing, and Cloud Computing and Virtualization
Resource scheduling assigns the precise and accurate task to CPU, network, and storage. The aim behind this is the extreme usage of resources. However, well organized scheduling is needed for both cloud providers and cloud users. This... more
Resource scheduling assigns the precise and accurate task to CPU, network, and storage. The aim behind this is the extreme usage of resources. However, well organized scheduling is needed for both cloud providers and cloud users. This paper is a chronological study of recent issues of resource scheduling in IaaS cloud computing environment. In our study, we investigate resource scheduling schemes and algorithms used by different researchers and categorize these approaches on the basis of problems addressed, schemes and the parameters used in evaluating different approaches. Based on various studies considered in this survey, we perceive that different schemes and algorithms did not consider some essential parameters and enhancement is requisite to improve the performance of the existing schemes. Furthermore, this study will trigger new and innovative methods of handling the problems of resource scheduling in the cloud and will help researchers in understanding the existing methodologies for the future adaptation and enhancements.
Research Interests:
Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment.... more
Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment. Methods/Statistical Analysis: Overall, a total of ninety-one studies from 1954 to 2015 have been reviewed in this paper. However, twenty-three studies are selected that focused on the meta-heuristic algorithms for their research. The selected papers are categorized into eight groups according to the optimization algorithms used. Findings: From the analytical study, we pointed out the various issues addressed (optimal and dynamically resource allocation, energy and QoS aware resource allocation, VM allocation and placement) through resource allocation meta-heuristics algorithms.Whereas, the improvement shows better performance concerns minimizing the execution and response time, energy consumption and cost while enhancing the efficiency and QoS in this environment. The comparison parameters (makespan 35%,execution time 13%, response time 26%, cost 22%, utilization21% and other 13% including energy, throughput etc) and also the experimental tools (CloudSim 43%, GridSim 5%, Simjava 9%, Matlab 9% and others 13%) used for evaluation of the various techniques for resource allocation in IaaS cloud computing. Applications/Improvements: The comprehensive review and systematic comparison of meta-heuristic resource allocation algorithms described in this appraisal will help researchers to analyze different techniques for future research directions.
Research Interests: Human Resource Management, Strategic Human Resource Management, Resource Allocation, Optimization techniques, Linear programming (Optimization Techniques), and 9 moreParticle Swarm Optimization, Cloud Computing, Meta-heuristics, Heuristic and Meta-heuristic Algorithms, Meta-Heuristics (Tabu Search, Genetic Algorithms, Simulated Annealing, Ant-Colony, Particle Swarm Optimization), Optimization Techniques: Genetic Algorithm; Differential Evolution, Particle Swarm Optimization PSO), Resource Utilization, and League Championship Algorithm
Spamming has attained a global dimension and continued to maintain an upward trend, both in sophistication and frequency. So far, it has defied every effort, including technical and non-technical proposals, to curb it. This study seeks to... more
Spamming has attained a global dimension and continued to maintain an upward trend, both in sophistication and frequency. So far, it has defied every effort, including technical and non-technical proposals, to curb it. This study seeks to investigate the prevalence of spam SMS, with focus on Nigeria. To quantify the prevalence, primary data was collected using questionnaire. Out of 270 surveyed, the responses of 191 mobile users were valid and analyzed. The study revealed that all mobile subscribers receive spam SMS, receiving an average of 2.45 spam SMS daily. This implies an average of 334,857,685 spam SMS received daily in Nigeria. However, most are for commercial purposes. Few mobile users report cases of fraudulent spam SMS, including those with SMShing intent, to network providers or security agencies. Most believe customers of mobile networks should reserve the right to determine the type of unsolicited SMS to be received, and unsolicited advertorial/promotional SMS should be regulated. Current guidelines and regulations need to be reviewed, to effectively manage spamming activities in Nigeria.
Research Interests:
3D map for mobile devices provide more realistic view of an environment and serves as better navigation aid. Previous research studies shows differences in 3D maps effect on acquiring of spatial knowledge. This is attributed to the... more
3D map for mobile devices provide more realistic view of an environment and serves as better navigation aid. Previous research studies shows differences in 3D maps effect on acquiring of spatial knowledge. This is attributed to the differences in mobile device computational capabilities. Crucial to this is the time it takes for 3D map dataset to be rendered for a required complete navigation task. Different findings suggest different approaches on solving the problem of time required for both in-core (inside mobile) and out-core (remote) rendering of 3D dataset. Unfortunately, studies on analytical techniques required to show the impact of computational resources required for the use of 3D map on mobile devices were neglected by the research communities. This paper uses Support Vector Machine (SVM) to analytically classified mobile device computational capabilities required for 3D map that will be suitable for use as navigation aid. Fifty different Smart phones were categorized on the bases of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy.
Research Interests:
Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. In cloud computing, a number of tasks may need to be scheduled on different virtual machines in order to minimize makespan... more
Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing.
In cloud computing, a number of tasks may need to be scheduled on different virtual machines in
order to minimize makespan and increase system utilization. Task scheduling problem is NP-complete,
hence finding an exact solution is intractable especially for large task sizes. This paper presents a Discrete
Symbiotic Organism Search (DSOS) algorithm for optimal scheduling of tasks on cloud resources.
Symbiotic Organism Search (SOS) is a newly developed metaheuristic optimization technique for solving
numerical optimization problems. SOS mimics the symbiotic relationships (mutualism, commensalism,
and parasitism) exhibited by organisms in an ecosystem. Simulation results revealed that DSOS
outperforms Particle Swarm Optimization (PSO) which is one of the most popular heuristic optimization
techniques used for task scheduling problems. DSOS converges faster when the search gets larger which
makes it suitable for large-scale scheduling problems. Analysis of the proposed method conducted using
t-test showed that DSOS performance is significantly better than that of PSO particularly for large search
space.
In cloud computing, a number of tasks may need to be scheduled on different virtual machines in
order to minimize makespan and increase system utilization. Task scheduling problem is NP-complete,
hence finding an exact solution is intractable especially for large task sizes. This paper presents a Discrete
Symbiotic Organism Search (DSOS) algorithm for optimal scheduling of tasks on cloud resources.
Symbiotic Organism Search (SOS) is a newly developed metaheuristic optimization technique for solving
numerical optimization problems. SOS mimics the symbiotic relationships (mutualism, commensalism,
and parasitism) exhibited by organisms in an ecosystem. Simulation results revealed that DSOS
outperforms Particle Swarm Optimization (PSO) which is one of the most popular heuristic optimization
techniques used for task scheduling problems. DSOS converges faster when the search gets larger which
makes it suitable for large-scale scheduling problems. Analysis of the proposed method conducted using
t-test showed that DSOS performance is significantly better than that of PSO particularly for large search
space.
Research Interests:
League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework for global optimization over a continuous search space first proposed by Ali Husseinzadeh Kashan in the year 2009. A general characteristic... more
League Championship Algorithm (LCA) is a sports-inspired population based algorithmic framework for global optimization over a continuous search space first proposed by Ali Husseinzadeh Kashan in the year 2009. A general characteristic between all population based optimization algorithms similar to the LCA is that, it tries to progress a population of achievable solutions to potential areas of the search space when seeking the optimization. In this paper, we proposed a job scheduling algorithm based on an enhanced LCA optimization technique for the infrastructure as a service (IaaS) cloud. Three other established algorithms i.e. First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All four algorithms assumed to be non-preemptive. The parameters used for this experiment are the average response time, average completion time and the makespan time. The results obtained shows that, LCA scheduling algorithm perform moderately better than the other algorithms as the number of virtual machines increases.
Research Interests:
The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential... more
The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.
Research Interests:
Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is... more
Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All algorithms under consideration assumed to be non-preemptive. The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud.
Research Interests:
Cloud computing technology keeps evolving especially in the area of service delivery for provisioning of computing and storage resources. Infrastructure as a service (IaaS) cloud is used to provide virtual machines on-demand to cloud... more
Cloud computing technology keeps evolving especially in the area of service delivery for provisioning of computing and storage resources. Infrastructure as a service (IaaS) cloud is used to provide virtual machines on-demand to cloud users. This Cloud computing power can be enhanced by setting up a traditional Grid network within the virtual machines to be used by clients as on-demand Grid or Grid as a Service (GaaS) Cloud. The purpose of this study is to survey the various concepts and architectures used for the on-demand GaaS using IaaS Cloud. The GaaS combines the advantage of providing clients with model that is familiar to the traditional Grid, the computational ability and the pay as you use paradigm of the cloud, with the capability of flexible management of comp utational resources. This research presented in this paper will contribute as a guide in the design of future architectures and in the selection of models or methods for further researches.
Research Interests:
League Championship Algorithm (LCA) is a population based algorithmic framework for global optimization over a continuous search space first proposed by Kashan (2009). It is a Swam optimization algorithm. A general characteristic between... more
League Championship Algorithm (LCA) is a population based algorithmic framework for global optimization over a continuous search space first proposed by Kashan (2009). It is a Swam optimization algorithm. A general characteristic between all population based optimization algorithms similar to the LCA is that they both try to progress a population of achievable solutions to potential areas of the search space when seeking the optimization. LCA is a newly proposed stochastic population based algorithm for continuous global optimization which tries to imitate a championship situation where synthetic football clubs participate in an artificial league for a number of weeks. This algorithm has been tested in many areas and performed creditably well as compared to other known optimization schemes or heuristics algorithms.
Research Interests:
Negative selection algorithms (NSAs) are inspired by artificial immune system. It creates techniques that aim at developing the immune based model. This is done by distinguishing self from non-self spam in the generation of detectors. In... more
Negative selection algorithms (NSAs) are inspired by artificial immune system. It creates techniques that aim at developing the immune based model. This is done by distinguishing self from non-self spam in the generation of detectors. In general, NSAs has an exponential run time. This research study the significance of time and accuracy for two commonly used matching rules. The hamming and r-chunk matching rules, based on different threshold values (r) for generating set of fixed number of detectors. The results show the differences between the mean values of time and accuracy for hamming and r-chunk matching rules. Statistical t test shows that the difference between hamming and r-chunk matching rule are insignificant for accuracy while it is significant for time.
Research Interests:
The Infrastructure as a service (IaaS) Cloud is a customer oriented cloud environment that offers user with computing infrastructures on-demand to be used based on the Cloud computing paradigm of pay-per-use. When the IaaS is now utilized... more
The Infrastructure as a service (IaaS) Cloud is a customer oriented cloud environment that offers user with computing infrastructures on-demand to be used based on the Cloud computing paradigm of pay-per-use. When the IaaS is now utilized to build a traditional Grid network within the cloud environment, it is now called an on-demand Grid as a service (GaaS) Cloud. In the on-demand GaaS Cloud, a user may use hundred of thousand of Grid nodes to implement a job, therefore manual scheduling is not a feasible scheduling solution. The main objective of this review is to study the various concepts and scheduling algorithms used for the on-demand GaaS Cloud in relation to the scheduling parameters used by existing researches. We also survey the Cloud infrastructures, Grid middlewares and the issues addressed by different researchers in the past within this domain of research. Our contribution will thus be of assistance in understanding the key scheduling algorithms and parameters for potential future enhancements in this evolving area of research.
Research Interests:
In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment.... more
In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner
automatically on-demand. Task execution failure is no longer accidental but a common characteristic of
cloud computing environment. In recent times, a number of intelligent scheduling techniques have been
used to address task scheduling issues in cloud without much attention to fault tolerance. In this research
article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique
for fault tolerance awareness to address cloud task execution which would reflect on the current available
resources and reduce the untimely failure of autonomous tasks. Experimental results show that our
proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate.
It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic
algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 %
in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the
experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to
improve the overall performance of the cloud environment.
automatically on-demand. Task execution failure is no longer accidental but a common characteristic of
cloud computing environment. In recent times, a number of intelligent scheduling techniques have been
used to address task scheduling issues in cloud without much attention to fault tolerance. In this research
article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique
for fault tolerance awareness to address cloud task execution which would reflect on the current available
resources and reduce the untimely failure of autonomous tasks. Experimental results show that our
proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate.
It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic
algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 %
in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the
experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to
improve the overall performance of the cloud environment.
Research Interests:
Fog computing is a recent model of computing, in a distributed way that extends the cloud computing operation to the network edges. Fog computing enables storage execution and tasks processing, which relies on the cooperation of users... more
Fog computing is a recent model of computing, in a distributed way that extends the cloud computing operation to the network edges. Fog computing enables storage execution and tasks processing, which relies on the cooperation of users and resource sharing among various devices. The fog being the new shift to cloud computing addresses some critical challenges associated with cloud model by providing notable advantages which are location sensitivity, latency minimization, geographical accessibility, wireless connectivity, mobility support and improved data streaming. Nevertheless, fog computing concept is never an option for replacing cloud computing model. In spite of the attractive solutions found in fog computing, it also inherited some security problems from the cloud. Most of the proposed techniques to solve security issues in fog computing could not completely addressed the security challenges due to the limitation of the various techniques. A fog computing security approach that is based on man- in- the middle attack using Dragonfly algorithm (DA) detection algorithm is conceptualized here. This paper is a framework for detection of MITM attacks that exist between the fog nodes and the cloud and vice versa using swarm intelligence optimization techniques called the DA algorithm which is be implemented on the platform of ifogsim simulator.
Research Interests:
Security Risk management in online shopping mall has significant influence on business performance. Regardless of the security risk, threats and vulnerabilities users are exposed to, customers and shoppers still find the e-commerce... more
Security Risk management in online shopping mall has significant influence on business performance. Regardless of the security risk, threats and vulnerabilities users are exposed to, customers and shoppers still find the e-commerce platform useful and most convenient. This study seeks to address user satisfaction in relation to security analysis and management using SHOPRITE Nigeria as a case study. Questionnaires were sent out and respondents data was collected via Whatsapp application. Using Cronbach's Alpha, the statistical reliability and accuracy outcome was determined. This study proved that customers are satisfied with the security risk management of SHOPRITE Nigeria and therefore feel safe to shop both online and offline
Research Interests:
Introduction This book will address both theories and empirical procedures for the applications of machine learning and data mining to solve problems in emerging trends in cyber dynamics. Cyber dynamics is a term used to describe... more
Introduction This book will address both theories and empirical procedures for the applications of machine learning and data mining to solve problems in emerging trends in cyber dynamics. Cyber dynamics is a term used to describe resilient algorithms, strategies, techniques and architectures for the development of the cyberspace environment such as cloud computing services, cyber security, data analytics, disruptive technologies like the blockchain, etc. The edited book intends to present new machine learning and data mining approaches in solving problems in emerging trends in cyber dynamics. Scope and Topics For better understanding by the readers, basic concepts, related work reviews, illustrations, empirical results and tables are expected to be integrated in each chapter to give the readers a maximum understanding and allow to easily follow the methodology and the results presented. The target audience of the edited book will come from different backgrounds. The audience will share and exchange novel knowledge, methods, industry experience and theories. The chapter contributions should described solving challenging issues using machine learning or data mining in any of the following domains: Blockchain Cryptocurrency Next generation cloud computing e.g. serverless, edge, fog, volunteer, etc. DeepFake detection and simulation IoT Ransomeware Cyber physical systems Social Media Internet of Vehicles, and others that may be suitable.