Jacob Sakhnini - Academia.edu (original) (raw)
Papers by Jacob Sakhnini
Conference of the Centre for Advanced Studies on Collaborative Research, 2019
The integration of sensors and communication technology in power systems, known as the smart grid... more The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.
Handbook of Big Data Privacy
Security of Cyber-Physical Systems, 2020
Phys. Commun., 2021
The massive integration of low-cost communication networks and Internet of Things (IoT) in today’... more The massive integration of low-cost communication networks and Internet of Things (IoT) in today’s cyber–physical grids has been accompanied by significant concerns regarding potential security threats. Specifically, wireless communication technology introduces additional vulnerability in terms of network security. In addition to cyber-security issues that have been investigated extensively, we must consider physical layer security. As such, considerable efforts have been employed toward developing a solution to address cyber-security issues. However, there are limited efforts on developing intrusion detection systems for physical layer security. In this paper, we propose an intelligent attack detection and identification model capable of classifying the attack type in the physical layer based on an ensemble of machine learning methods. Furthermore, the proposed model localizes the attack or fault to specific features or measurements in the system to assist cyber-security profession...
Much of the recent innovation and development in technology is geared towards the integration of ... more Much of the recent innovation and development in technology is geared towards the integration of communication networks among systems and devices. Various applications of technology are witnessing a shift to internet-linked components and integrating cyber and physical systems together; such phenomenon is often referred to as Cyber Physical Systems (CPS). CPS is used in many applications including industrial control systems and critical infrastructure such as health-care and power generation. The increased integration of CPS and internet networks raises security concerns and vulnerabilities. This book delves into some of the security challenges associated with CPS as well as intelligent methods used to secure CPS in various applications. The book also discusses various AI-based methods for enhanced CPS security and performance and presents case studies and proof of concepts in simulated environments.
Smart Cyber Physical Grids are the new wave of power system technology that integrates networks o... more Smart Cyber Physical Grids are the new wave of power system technology that integrates networks of sensors with power stations for more efficient power generation and distribution. While utilizing communication networks is accompanied with tremendous advantages, it also increases the vulnerability of power systems to cyber attacks. Many methods for security and attack detection have been proposed in literature; however, most papers do not consider the imbalance of data in real power systems. In this paper, we propose a deep learning based method, referred to as Ensemble Stacked AutoEncoder (ESAE), aimed at tackling the problem of data imbalance. This method achieves superior performance on imbalanced data by developing a deep representation learning model to construct new balanced representations. The detection accuracy and model performance is improved by utilizing an ensemble architecture based on Stacked Autoencoders and Random Forest classifiers to detect attacks from the new re...
The integration of sensors and communication technology in power systems, known as the smart grid... more The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.
2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE)
False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Dete... more False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.
Conference of the Centre for Advanced Studies on Collaborative Research, 2019
The integration of sensors and communication technology in power systems, known as the smart grid... more The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.
Handbook of Big Data Privacy
Security of Cyber-Physical Systems, 2020
Phys. Commun., 2021
The massive integration of low-cost communication networks and Internet of Things (IoT) in today’... more The massive integration of low-cost communication networks and Internet of Things (IoT) in today’s cyber–physical grids has been accompanied by significant concerns regarding potential security threats. Specifically, wireless communication technology introduces additional vulnerability in terms of network security. In addition to cyber-security issues that have been investigated extensively, we must consider physical layer security. As such, considerable efforts have been employed toward developing a solution to address cyber-security issues. However, there are limited efforts on developing intrusion detection systems for physical layer security. In this paper, we propose an intelligent attack detection and identification model capable of classifying the attack type in the physical layer based on an ensemble of machine learning methods. Furthermore, the proposed model localizes the attack or fault to specific features or measurements in the system to assist cyber-security profession...
Much of the recent innovation and development in technology is geared towards the integration of ... more Much of the recent innovation and development in technology is geared towards the integration of communication networks among systems and devices. Various applications of technology are witnessing a shift to internet-linked components and integrating cyber and physical systems together; such phenomenon is often referred to as Cyber Physical Systems (CPS). CPS is used in many applications including industrial control systems and critical infrastructure such as health-care and power generation. The increased integration of CPS and internet networks raises security concerns and vulnerabilities. This book delves into some of the security challenges associated with CPS as well as intelligent methods used to secure CPS in various applications. The book also discusses various AI-based methods for enhanced CPS security and performance and presents case studies and proof of concepts in simulated environments.
Smart Cyber Physical Grids are the new wave of power system technology that integrates networks o... more Smart Cyber Physical Grids are the new wave of power system technology that integrates networks of sensors with power stations for more efficient power generation and distribution. While utilizing communication networks is accompanied with tremendous advantages, it also increases the vulnerability of power systems to cyber attacks. Many methods for security and attack detection have been proposed in literature; however, most papers do not consider the imbalance of data in real power systems. In this paper, we propose a deep learning based method, referred to as Ensemble Stacked AutoEncoder (ESAE), aimed at tackling the problem of data imbalance. This method achieves superior performance on imbalanced data by developing a deep representation learning model to construct new balanced representations. The detection accuracy and model performance is improved by utilizing an ensemble architecture based on Stacked Autoencoders and Random Forest classifiers to detect attacks from the new re...
The integration of sensors and communication technology in power systems, known as the smart grid... more The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.
2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE)
False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Dete... more False Data Injection (FDI) attacks are a common form of Cyber-attack targetting smart grids. Detection of stealthy FDI attacks is impossible by the current bad data detection systems. Machine learning is one of the alternative methods proposed to detect FDI attacks. This paper analyzes three various supervised learning techniques, each to be used with three different feature selection (FS) techniques. These methods are tested on the IEEE 14-bus, 57-bus, and 118-bus systems for evaluation of versatility. Accuracy of the classification is used as the main evaluation method for each detection technique. Simulation study clarify the supervised learning combined with heuristic FS methods result in an improved performance of the classification algorithms for FDI attack detection.