Proceedings of 2nd International Conference on Computer Vision & Image Processing, 2018
Deep convolutional neural networks are becoming increasingly popular in large-scale image recogni... more Deep convolutional neural networks are becoming increasingly popular in large-scale image recognition, classification, localization, and detection. In this paper, the performance of state-of-the-art convolution neural networks (ConvNets) models of the ImageNet challenge (ILSVRC), namely VGG16, VGG19, OverFeat, ResNet50, and Inception-v3 which achieved top-5 error rates up to 4.2% are analyzed in the context of face recognition. Instead of using handcrafted feature extraction techniques which requires a domain-level understanding, ConvNets have the advantages of automatically learning complex features, more training time, and less evaluation time. These models are benchmarked on AR and Extended Yale B face dataset with five performance metrics, namely Precision, Recall, F1-score, Rank-1 accuracy, and Rank-5 accuracy. It is found that GoogleNet ConvNets model with Inception-v3 architecture outperforms than other four architectures with a Rank-1 accuracy of 98.46% on AR face dataset and 97.94% accuracy on Extended Yale B face dataset. It confirms that deep CNN architectures are suitable for real-time face recognition in the future.
Cloud computing usage has increased rapidly in both industries and in research. In recent days as... more Cloud computing usage has increased rapidly in both industries and in research. In recent days as the data grows rapidly, in order to meet the business needs federated cloud is adopted. In federated cloud, as the data is stored and processed away from the user and the cloud service provider, privacy and integrity of the data plays a crucial role. This paper proposes a practical and efficient method for providing security to the data stored at the federated cloud environment using homomorphic techniques. This method provides security by storing the encrypted data in the cloud. The cipher key which is generated for encrypting the data plays a major role. This paper explores important aspects within this context and examines the role of metadata in data security which improves the performance in a secured manner. The proposed novel homomorphic based key distribution protocol is the key area under focus. This proposed work aims to promote the use of homomorphism in multi-clouds due to i...
Cloud computing has gained the popularity of todays IT sector because of the low cost involved in... more Cloud computing has gained the popularity of todays IT sector because of the low cost involved in setup, ease of resource configuration and maintenance. The increase in the number of cloud providers in the market has led to availability of a wide range of cloud solutions offered to the consumers. These solutions are based on different cloud architectures and usually are incompatible with each other. It is very hard to find a single provider which offers all services the end users need. Cloud providers offer proprietary solutions that force cloud customers to decide the design and deployment models as well as the technology at the early stages of software development. One of the major issues of this paradigm is; the applications and services hosted with a specific cloud provider are locked to their specific implementation technique and operational methods. Hence, moving these applications and services to another provider is a tedious task. This situation is often termed as vendor loc...
2019 International Carnahan Conference on Security Technology (ICCST), 2019
Phishing is a criminal offense which involves theft of user’s sensitive data. The phishing websit... more Phishing is a criminal offense which involves theft of user’s sensitive data. The phishing websites target individuals, organizations, the cloud storage hosting sites and government websites. Currently, hardware based approaches for anti-phishing is widely used but due to the cost and operational factors software based approaches are preferred. The existing phishing detection approaches fails to provide solution to problem like zero-day phishing website attacks. To overcome these issues and precisely detect phishing occurrence a three phase attack detection named as Web Crawler based Phishing Attack Detector(WC-PAD) has been proposed. It takes the web traffics, web content and Uniform Resource Locator(URL) as input features, based on these features classification of phishing and non phishing websites are done. The experimental analysis of the proposed WC-PAD is done with datasets collected from real phishing cases. From the experimental results, it is found that the proposed WC-PAD gives 98.9% accuracy in both phishing and zero-day phishing attack detection.
2018 Tenth International Conference on Advanced Computing (ICoAC), 2018
Detecting anomalies in the Traffic Systems could be very useful for the analysis of traffic rule ... more Detecting anomalies in the Traffic Systems could be very useful for the analysis of traffic rule violation, fault detection and other traffic-related issues. In this paper trajectory-based anomaly detection using spatial temporal analysis, K-means, linear regression, z score and Hierarchical temporal memory clustering algorithm are analyzed. The spatial localization of an object is considered as an event. Traffic anomaly detection rules are formulated in three levels: Point anomaly, Sequential anomaly and Co-occurrence anomaly. This paper analyses the performance of various traffic anomaly detection methodologies in terms of accuracy to reduce false alarm.
Advances in Intelligent Systems and Computing, 2018
Cluster-Based Routing Protocol (CBRP) is popular and proven for energy efficiency in Mobile Ad ho... more Cluster-Based Routing Protocol (CBRP) is popular and proven for energy efficiency in Mobile Ad hoc Networks (MANET). CBRP protocol divides the complete network into a number of clusters. Each cluster contains Cluster Head (CH) which maintains the cluster formation. Existence of CH improves routing performance in terms of reduction in routing overhead and power consumption. However, due to the mobility of the network, movement of the CH and cluster members, re-clustering is required and this increases overhead in the formation of clusters. The stability of the CH is an important factor for the stability of the cluster. Hence CH selection should be done efficiently such that the CH survives for a longer time. Existing CH selection algorithms use weight based approach which uses parameters like battery power, mobility, residual energy, and node degree to calculate the weight. Of all these parameters, mobility is an important factor in MANET and it has to be given more importance. Hence this paper proposes a Modified Energy-Efficient Stable Clustering (MEESC) algorithm in which node mobility is given more importance in weight calculation for the selection of CH. The proposed algorithm is simulated in NS3 and found to give better results in CH selection in terms of number of clusters formed and lifetime of the cluster head.
Abstract The amount of data and computing power has drastically increased over the last decade, w... more Abstract The amount of data and computing power has drastically increased over the last decade, which leads to the development of several new fronts in the field of Natural Language Processing (NLP). In addition to that, the entanglement of embeddings and large pre-trained models have pushed the field forward, covering a wide variety of tasks starting from machine translation to more complex tasks such as contextual text classification. This paper covers the underlying idea behind all embeddings and pre-trained models and provides an insight into fundamental strategies and implementation details of innovative embeddings. Further, it imparts the pros and cons of each specific embedding design and the associated impact on the result. It also comprehends the comparison of all the different strategies, datasets, architectures discussed in different papers with the help of standard metrics used in NLP. The content covered in this review work aims to shed light on different milestones reached in NLP, allowing the reader to deepen their understanding of NLP, which would motivate to explore the field further.
Detection of abnormal events in the traffic scene is very challenging and is a significant proble... more Detection of abnormal events in the traffic scene is very challenging and is a significant problem in video surveillance. The authors proposed a novel scheme called super orientation optical flow (SOOF)-based clustering for identifying the abnormal activities. The key idea behind the proposed SOOF features is to efficiently reproduce the motion information of a moving vehicle with respect to superorientation motion descriptor within the sequence of the frame. Here, the authors adopt the mean absolute temporal difference to identify the anomalies by motion block (MB) selection and localisation. SOOF features obtained from MB are used as motion descriptor for both normal and abnormal events. Simple and efficient K-means clustering is used to study the normal motion flow during the training. The abnormal events are identified using the nearest-neighbour searching technique in the testing phase. The experimental outcome shows that the proposed work is effectively detecting anomalies and found to give results better than the state-of-the-art techniques.
The growing use of wireless technology in healthcare systems and devices makes these systems part... more The growing use of wireless technology in healthcare systems and devices makes these systems particularly open to cyber-based attacks, including denial of service and information theft via sniffing (eaves-dropping) and phishing attacks. Evolving technology enables wireless healthcare systems to communicate over longer ranges, which opens them up to greater numbers of possible threats. Unmanned aerial vehicles (UAV) or drones present a new and evolving attack surface for compromising wireless healthcare systems. An enumeration of the types of wireless attacks capable via drones are presented, including two new types of cyber threats: a stepping stone attack and a cloud-enabled attack. A real UAV is developed to test and demonstrate the vulnerabilities of healthcare systems to this new threat vector. The UAV successfully attacked a simulated smart hospital environment and also a small collection of wearable healthcare sensors. Compromise of wearable or implanted medical devices can lead to increased morbidity and mortality.
Abstract In recent days, malwares are advanced, sophisticatedly engineered to attack the target. ... more Abstract In recent days, malwares are advanced, sophisticatedly engineered to attack the target. Most of such advanced malwares are highly persistent and capable of escaping from the security systems. This paper explores such an advanced malware type called Advanced Persistent Threats (APTs). APTs pave the way for most of the Cyber espionages and sabotages. APTs are highly sophisticated, target specific and operate in a stealthy mode till the target is compromised. The intention of the APTs is to deploy target specific automated malwares in a host or network to initiate an on-demand attack based on continuous monitoring. Encrypted covert communication and advanced, sophisticated attack techniques make the identification of APTs more challenging. Conventional security systems like antivirus, anti-malware systems which depend on signatures and static analysis fail to identify these APTs. The Advanced Evasive Techniques (AET) used in APTs are capable of bypassing the stateful firewalls housed in the enterprise choke points at ease. Hence, this paper presents a detailed study on sophisticated attack and evasion techniques used by the contemporary malwares. Furthermore, existing malware analysis techniques, application hardening techniques and CPU assisted application security schemes are also discussed. Finally, the study concludes by presenting the System and Network Security Design (SNSD) using existing mitigation techniques.
International Journal of Intelligent Information Technologies, 2018
Advanced persistent threats (APT) are major threats in the field of system and network security. ... more Advanced persistent threats (APT) are major threats in the field of system and network security. They are extremely stealthy and use advanced evasion techniques like packing and behaviour obfuscation to hide their malicious behaviour and evade the detection methods. Existing behavior-based detection technique fails to detect the APTs due to its high persistence mechanism and sophisticated code nature. Hence, a novel hybrid analysis technique using Behavior based Sandboxing approach is proposed. The proposed technique consists of four phases namely, Static, Dynamic, Memory and System state analysis. Initially, static analysis is performed on the sample which involves packer detection and signature verification. If the sample is found stealthy and remains undetected, then it is executed inside a sandbox environment to analyze its behavior. Further, memory analysis is performed to extract memory artefacts of the current system state. Finally, system state analysis is performed by corre...
International Journal of Intelligent Information Technologies, 2018
Face recognition systems are in great demand for domestic and commercial applications. A novel fe... more Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better ...
Proceedings of 2nd International Conference on Computer Vision & Image Processing, 2018
Deep convolutional neural networks are becoming increasingly popular in large-scale image recogni... more Deep convolutional neural networks are becoming increasingly popular in large-scale image recognition, classification, localization, and detection. In this paper, the performance of state-of-the-art convolution neural networks (ConvNets) models of the ImageNet challenge (ILSVRC), namely VGG16, VGG19, OverFeat, ResNet50, and Inception-v3 which achieved top-5 error rates up to 4.2% are analyzed in the context of face recognition. Instead of using handcrafted feature extraction techniques which requires a domain-level understanding, ConvNets have the advantages of automatically learning complex features, more training time, and less evaluation time. These models are benchmarked on AR and Extended Yale B face dataset with five performance metrics, namely Precision, Recall, F1-score, Rank-1 accuracy, and Rank-5 accuracy. It is found that GoogleNet ConvNets model with Inception-v3 architecture outperforms than other four architectures with a Rank-1 accuracy of 98.46% on AR face dataset and 97.94% accuracy on Extended Yale B face dataset. It confirms that deep CNN architectures are suitable for real-time face recognition in the future.
Cloud computing usage has increased rapidly in both industries and in research. In recent days as... more Cloud computing usage has increased rapidly in both industries and in research. In recent days as the data grows rapidly, in order to meet the business needs federated cloud is adopted. In federated cloud, as the data is stored and processed away from the user and the cloud service provider, privacy and integrity of the data plays a crucial role. This paper proposes a practical and efficient method for providing security to the data stored at the federated cloud environment using homomorphic techniques. This method provides security by storing the encrypted data in the cloud. The cipher key which is generated for encrypting the data plays a major role. This paper explores important aspects within this context and examines the role of metadata in data security which improves the performance in a secured manner. The proposed novel homomorphic based key distribution protocol is the key area under focus. This proposed work aims to promote the use of homomorphism in multi-clouds due to i...
Cloud computing has gained the popularity of todays IT sector because of the low cost involved in... more Cloud computing has gained the popularity of todays IT sector because of the low cost involved in setup, ease of resource configuration and maintenance. The increase in the number of cloud providers in the market has led to availability of a wide range of cloud solutions offered to the consumers. These solutions are based on different cloud architectures and usually are incompatible with each other. It is very hard to find a single provider which offers all services the end users need. Cloud providers offer proprietary solutions that force cloud customers to decide the design and deployment models as well as the technology at the early stages of software development. One of the major issues of this paradigm is; the applications and services hosted with a specific cloud provider are locked to their specific implementation technique and operational methods. Hence, moving these applications and services to another provider is a tedious task. This situation is often termed as vendor loc...
2019 International Carnahan Conference on Security Technology (ICCST), 2019
Phishing is a criminal offense which involves theft of user’s sensitive data. The phishing websit... more Phishing is a criminal offense which involves theft of user’s sensitive data. The phishing websites target individuals, organizations, the cloud storage hosting sites and government websites. Currently, hardware based approaches for anti-phishing is widely used but due to the cost and operational factors software based approaches are preferred. The existing phishing detection approaches fails to provide solution to problem like zero-day phishing website attacks. To overcome these issues and precisely detect phishing occurrence a three phase attack detection named as Web Crawler based Phishing Attack Detector(WC-PAD) has been proposed. It takes the web traffics, web content and Uniform Resource Locator(URL) as input features, based on these features classification of phishing and non phishing websites are done. The experimental analysis of the proposed WC-PAD is done with datasets collected from real phishing cases. From the experimental results, it is found that the proposed WC-PAD gives 98.9% accuracy in both phishing and zero-day phishing attack detection.
2018 Tenth International Conference on Advanced Computing (ICoAC), 2018
Detecting anomalies in the Traffic Systems could be very useful for the analysis of traffic rule ... more Detecting anomalies in the Traffic Systems could be very useful for the analysis of traffic rule violation, fault detection and other traffic-related issues. In this paper trajectory-based anomaly detection using spatial temporal analysis, K-means, linear regression, z score and Hierarchical temporal memory clustering algorithm are analyzed. The spatial localization of an object is considered as an event. Traffic anomaly detection rules are formulated in three levels: Point anomaly, Sequential anomaly and Co-occurrence anomaly. This paper analyses the performance of various traffic anomaly detection methodologies in terms of accuracy to reduce false alarm.
Advances in Intelligent Systems and Computing, 2018
Cluster-Based Routing Protocol (CBRP) is popular and proven for energy efficiency in Mobile Ad ho... more Cluster-Based Routing Protocol (CBRP) is popular and proven for energy efficiency in Mobile Ad hoc Networks (MANET). CBRP protocol divides the complete network into a number of clusters. Each cluster contains Cluster Head (CH) which maintains the cluster formation. Existence of CH improves routing performance in terms of reduction in routing overhead and power consumption. However, due to the mobility of the network, movement of the CH and cluster members, re-clustering is required and this increases overhead in the formation of clusters. The stability of the CH is an important factor for the stability of the cluster. Hence CH selection should be done efficiently such that the CH survives for a longer time. Existing CH selection algorithms use weight based approach which uses parameters like battery power, mobility, residual energy, and node degree to calculate the weight. Of all these parameters, mobility is an important factor in MANET and it has to be given more importance. Hence this paper proposes a Modified Energy-Efficient Stable Clustering (MEESC) algorithm in which node mobility is given more importance in weight calculation for the selection of CH. The proposed algorithm is simulated in NS3 and found to give better results in CH selection in terms of number of clusters formed and lifetime of the cluster head.
Abstract The amount of data and computing power has drastically increased over the last decade, w... more Abstract The amount of data and computing power has drastically increased over the last decade, which leads to the development of several new fronts in the field of Natural Language Processing (NLP). In addition to that, the entanglement of embeddings and large pre-trained models have pushed the field forward, covering a wide variety of tasks starting from machine translation to more complex tasks such as contextual text classification. This paper covers the underlying idea behind all embeddings and pre-trained models and provides an insight into fundamental strategies and implementation details of innovative embeddings. Further, it imparts the pros and cons of each specific embedding design and the associated impact on the result. It also comprehends the comparison of all the different strategies, datasets, architectures discussed in different papers with the help of standard metrics used in NLP. The content covered in this review work aims to shed light on different milestones reached in NLP, allowing the reader to deepen their understanding of NLP, which would motivate to explore the field further.
Detection of abnormal events in the traffic scene is very challenging and is a significant proble... more Detection of abnormal events in the traffic scene is very challenging and is a significant problem in video surveillance. The authors proposed a novel scheme called super orientation optical flow (SOOF)-based clustering for identifying the abnormal activities. The key idea behind the proposed SOOF features is to efficiently reproduce the motion information of a moving vehicle with respect to superorientation motion descriptor within the sequence of the frame. Here, the authors adopt the mean absolute temporal difference to identify the anomalies by motion block (MB) selection and localisation. SOOF features obtained from MB are used as motion descriptor for both normal and abnormal events. Simple and efficient K-means clustering is used to study the normal motion flow during the training. The abnormal events are identified using the nearest-neighbour searching technique in the testing phase. The experimental outcome shows that the proposed work is effectively detecting anomalies and found to give results better than the state-of-the-art techniques.
The growing use of wireless technology in healthcare systems and devices makes these systems part... more The growing use of wireless technology in healthcare systems and devices makes these systems particularly open to cyber-based attacks, including denial of service and information theft via sniffing (eaves-dropping) and phishing attacks. Evolving technology enables wireless healthcare systems to communicate over longer ranges, which opens them up to greater numbers of possible threats. Unmanned aerial vehicles (UAV) or drones present a new and evolving attack surface for compromising wireless healthcare systems. An enumeration of the types of wireless attacks capable via drones are presented, including two new types of cyber threats: a stepping stone attack and a cloud-enabled attack. A real UAV is developed to test and demonstrate the vulnerabilities of healthcare systems to this new threat vector. The UAV successfully attacked a simulated smart hospital environment and also a small collection of wearable healthcare sensors. Compromise of wearable or implanted medical devices can lead to increased morbidity and mortality.
Abstract In recent days, malwares are advanced, sophisticatedly engineered to attack the target. ... more Abstract In recent days, malwares are advanced, sophisticatedly engineered to attack the target. Most of such advanced malwares are highly persistent and capable of escaping from the security systems. This paper explores such an advanced malware type called Advanced Persistent Threats (APTs). APTs pave the way for most of the Cyber espionages and sabotages. APTs are highly sophisticated, target specific and operate in a stealthy mode till the target is compromised. The intention of the APTs is to deploy target specific automated malwares in a host or network to initiate an on-demand attack based on continuous monitoring. Encrypted covert communication and advanced, sophisticated attack techniques make the identification of APTs more challenging. Conventional security systems like antivirus, anti-malware systems which depend on signatures and static analysis fail to identify these APTs. The Advanced Evasive Techniques (AET) used in APTs are capable of bypassing the stateful firewalls housed in the enterprise choke points at ease. Hence, this paper presents a detailed study on sophisticated attack and evasion techniques used by the contemporary malwares. Furthermore, existing malware analysis techniques, application hardening techniques and CPU assisted application security schemes are also discussed. Finally, the study concludes by presenting the System and Network Security Design (SNSD) using existing mitigation techniques.
International Journal of Intelligent Information Technologies, 2018
Advanced persistent threats (APT) are major threats in the field of system and network security. ... more Advanced persistent threats (APT) are major threats in the field of system and network security. They are extremely stealthy and use advanced evasion techniques like packing and behaviour obfuscation to hide their malicious behaviour and evade the detection methods. Existing behavior-based detection technique fails to detect the APTs due to its high persistence mechanism and sophisticated code nature. Hence, a novel hybrid analysis technique using Behavior based Sandboxing approach is proposed. The proposed technique consists of four phases namely, Static, Dynamic, Memory and System state analysis. Initially, static analysis is performed on the sample which involves packer detection and signature verification. If the sample is found stealthy and remains undetected, then it is executed inside a sandbox environment to analyze its behavior. Further, memory analysis is performed to extract memory artefacts of the current system state. Finally, system state analysis is performed by corre...
International Journal of Intelligent Information Technologies, 2018
Face recognition systems are in great demand for domestic and commercial applications. A novel fe... more Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better ...
Uploads
Papers by Vaidehi Vijayakumar