Using a face validity approach, this paper provides a validation of the Database Forensic Investi... more Using a face validity approach, this paper provides a validation of the Database Forensic Investigation Metamodel (DBFIM). The DBFIM was developed to solve interoperability, heterogeneity, complexity, and ambiguity in the database forensic investigation (DBFI) field, where several models were identified, collected, and reviewed to develop DBFIM. However, the developed DBFIM lacked the face validity-based approach that could ensure DBFIM’s applicability in the DBFI field. The completeness, usefulness, and logic of the developed DBFIM needed to be validated by experts. Therefore, the objective of this paper is to perform the validation of the developed DBFIM using the qualitative face validity approach. The face validity method is a common way of validating metamodels through subject expert inquiry on the domain application of the metamodel to assess whether the metamodel is reasonable and compatible based on the outcomes. For this purpose, six experts were nominated and selected to v...
2018 IEEE Conference on Application, Information and Network Security (AINS), 2018
many specific database forensic investigation models have been proposed in the literature to solv... more many specific database forensic investigation models have been proposed in the literature to solve database incidents, but these models have various redundant investigation processes, concepts, activities, and tasks which make database forensic investigation domain unstructured, heterogeneous, and complex among domain practitioners. Recent works on database forensic investigation domain are motivated due to the successes of combining shared metadata in one platform called metamodel to facilitate in managing, sharing, and reusing the shared metadata (knowledge) in different database scenarios. In this paper, a novel model derivation system called Model Driven Database Forensic Investigation System (MDDBFIS) was proposed to work with existing database forensic investigation metamodel to facilitate in managing the domain knowledge amongst domain practitioners. The Design Science Research Methodology (DSRM) has been adapted to design and develop the MDDBFIS. The effectiveness of the MDDBFIS was demonstrated in deriving a solution model from database forensic investigation metamodel to solve a real case study, and the results were evaluated by fully digital forensic experts at CyberSecurity Malaysia.
Lecture Notes on Data Engineering and Communications Technologies, 2021
Security has a major role to play in the utilization and operations of the internet of things (Io... more Security has a major role to play in the utilization and operations of the internet of things (IoT). Several studies have explored anomaly intrusion detection and its utilization in a variety of applications. Building an effective anomaly intrusion detection system requires researchers and developers to comprehend the complex structure from noisy data, identify the dynamic anomaly patterns, and detect anomalies while lacking sufficient labels. Consequently, improving the performance of anomaly detection requires the use of advanced deep learning techniques instead of traditional shallow learning approaches. The large number of devices connected to IoT which massively generate a large amount of data require large computation as well. This study presents a survey on anomaly intrusion detection using deep learning approaches with emphasis on resource-constrained devices used in real-world problems in the realm of IoT. The findings from the reviewed studies showed that deep learning is superior to detect anomaly in terms of high detection accuracy and false alarm rate. However, it is highly recommended to conduct further studies using deep learning techniques for robust IDS.
Advances in Data Science and Adaptive Analysis, 2021
The rise of the Internet of things (IoT) provides an intelligent environment. However, it demands... more The rise of the Internet of things (IoT) provides an intelligent environment. However, it demands a higher security effort due to its vulnerabilities. A number of studies have emphasized the area of anomaly intrusion detection and its use in various types of applications. The development of a robust anomaly intrusion detection system relies heavily on understanding complex structures from noisy data, identifying dynamic anomaly patterns, and detecting anomalies with insufficient labels. Therefore, an advanced approach of deep learning techniques is required for the purpose of achieving improved performance of anomaly detection rather than the unconventional approaches of shallow learning. However, the immense adaptations of IoT in major devices result in an increase in data usage and higher computational requirements. Hence, this work highlights a review on anomaly intrusion detection utilizing deep learning approaches with a focus on resource-constrained devices’ application within...
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide ra... more The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securin...
The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digita... more The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digital forensic models using the Design Science Research method. The outcome of this study generated in-depth knowledge of the research challenges and opportunities through which an effective investigation can be carried out on drone-related incidents. Furthermore, a potential generic investigation model has been proposed. The findings presented in this study are essentially relevant to forensic re...
Database Forensics (DBF) is a widespread area of knowledge. It has many complex features and is w... more Database Forensics (DBF) is a widespread area of knowledge. It has many complex features and is well known amongst database investigators and practitioners. Several models and frameworks have been created specifically to allow knowledge-sharing and effective DBF activities. However, these are often narrow in focus and address specified database incident types. We have analysed 60 such models in an attempt to uncover how numerous DBF activities are really public even when the actions vary. We then generate a unified abstract view of DBF in the form of a metamodel. We identified, extracted, and proposed a common concept and reconciled concept definitions to propose a metamodel. We have applied a metamodelling process to guarantee that this metamodel is comprehensive and consistent.
Using a face validity approach, this paper provides a validation of the Database Forensic Investi... more Using a face validity approach, this paper provides a validation of the Database Forensic Investigation Metamodel (DBFIM). The DBFIM was developed to solve interoperability, heterogeneity, complexity, and ambiguity in the database forensic investigation (DBFI) field, where several models were identified, collected, and reviewed to develop DBFIM. However, the developed DBFIM lacked the face validity-based approach that could ensure DBFIM’s applicability in the DBFI field. The completeness, usefulness, and logic of the developed DBFIM needed to be validated by experts. Therefore, the objective of this paper is to perform the validation of the developed DBFIM using the qualitative face validity approach. The face validity method is a common way of validating metamodels through subject expert inquiry on the domain application of the metamodel to assess whether the metamodel is reasonable and compatible based on the outcomes. For this purpose, six experts were nominated and selected to v...
2018 IEEE Conference on Application, Information and Network Security (AINS), 2018
many specific database forensic investigation models have been proposed in the literature to solv... more many specific database forensic investigation models have been proposed in the literature to solve database incidents, but these models have various redundant investigation processes, concepts, activities, and tasks which make database forensic investigation domain unstructured, heterogeneous, and complex among domain practitioners. Recent works on database forensic investigation domain are motivated due to the successes of combining shared metadata in one platform called metamodel to facilitate in managing, sharing, and reusing the shared metadata (knowledge) in different database scenarios. In this paper, a novel model derivation system called Model Driven Database Forensic Investigation System (MDDBFIS) was proposed to work with existing database forensic investigation metamodel to facilitate in managing the domain knowledge amongst domain practitioners. The Design Science Research Methodology (DSRM) has been adapted to design and develop the MDDBFIS. The effectiveness of the MDDBFIS was demonstrated in deriving a solution model from database forensic investigation metamodel to solve a real case study, and the results were evaluated by fully digital forensic experts at CyberSecurity Malaysia.
Lecture Notes on Data Engineering and Communications Technologies, 2021
Security has a major role to play in the utilization and operations of the internet of things (Io... more Security has a major role to play in the utilization and operations of the internet of things (IoT). Several studies have explored anomaly intrusion detection and its utilization in a variety of applications. Building an effective anomaly intrusion detection system requires researchers and developers to comprehend the complex structure from noisy data, identify the dynamic anomaly patterns, and detect anomalies while lacking sufficient labels. Consequently, improving the performance of anomaly detection requires the use of advanced deep learning techniques instead of traditional shallow learning approaches. The large number of devices connected to IoT which massively generate a large amount of data require large computation as well. This study presents a survey on anomaly intrusion detection using deep learning approaches with emphasis on resource-constrained devices used in real-world problems in the realm of IoT. The findings from the reviewed studies showed that deep learning is superior to detect anomaly in terms of high detection accuracy and false alarm rate. However, it is highly recommended to conduct further studies using deep learning techniques for robust IDS.
Advances in Data Science and Adaptive Analysis, 2021
The rise of the Internet of things (IoT) provides an intelligent environment. However, it demands... more The rise of the Internet of things (IoT) provides an intelligent environment. However, it demands a higher security effort due to its vulnerabilities. A number of studies have emphasized the area of anomaly intrusion detection and its use in various types of applications. The development of a robust anomaly intrusion detection system relies heavily on understanding complex structures from noisy data, identifying dynamic anomaly patterns, and detecting anomalies with insufficient labels. Therefore, an advanced approach of deep learning techniques is required for the purpose of achieving improved performance of anomaly detection rather than the unconventional approaches of shallow learning. However, the immense adaptations of IoT in major devices result in an increase in data usage and higher computational requirements. Hence, this work highlights a review on anomaly intrusion detection utilizing deep learning approaches with a focus on resource-constrained devices’ application within...
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide ra... more The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securin...
The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digita... more The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digital forensic models using the Design Science Research method. The outcome of this study generated in-depth knowledge of the research challenges and opportunities through which an effective investigation can be carried out on drone-related incidents. Furthermore, a potential generic investigation model has been proposed. The findings presented in this study are essentially relevant to forensic re...
Database Forensics (DBF) is a widespread area of knowledge. It has many complex features and is w... more Database Forensics (DBF) is a widespread area of knowledge. It has many complex features and is well known amongst database investigators and practitioners. Several models and frameworks have been created specifically to allow knowledge-sharing and effective DBF activities. However, these are often narrow in focus and address specified database incident types. We have analysed 60 such models in an attempt to uncover how numerous DBF activities are really public even when the actions vary. We then generate a unified abstract view of DBF in the form of a metamodel. We identified, extracted, and proposed a common concept and reconciled concept definitions to propose a metamodel. We have applied a metamodelling process to guarantee that this metamodel is comprehensive and consistent.
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