scholarly journals Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 29429-29440 ◽  
Author(s):  
Mohammad Ghiasi ◽  
Moslem Dehghani ◽  
Taher Niknam ◽  
Abdollah Kavousi-Fard ◽  
Pierluigi Siano ◽  
...  
Keyword(s):  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attack ◽  
Dc Microgrid ◽  
Hilbert Huang Transform ◽  
Security Enhancement ◽  
Blockchain Technology

Cyber Security Aspects of Virtualization in Cloud Computing Environments

2021 ◽  
pp. 1658-1671
Author(s):  
Darshan Mansukhbhai Tank ◽  
Akshai Aggarwal ◽  
Nirbhay Kumar Chaubey
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Point Of View ◽  
Cyber Attack ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Approach ◽  
Detection Response

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.

Cyber Security Aspects of Virtualization in Cloud Computing Environments

2020 ◽  
pp. 283-299 ◽  
Author(s):  
Darshan Mansukhbhai Tank ◽  
Akshai Aggarwal ◽  
Nirbhay Kumar Chaubey
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Point Of View ◽  
Cyber Attack ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Approach ◽  
Detection Response

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.

scholarly journals Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1311
Author(s):  
Qiyi He ◽  
Xiaolin Meng ◽  
Rong Qu ◽  
Ruijie Xi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Cyber Security ◽  
Unified Modeling Language ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Cyber Attack ◽  
Tree Model ◽  
Data Set

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection.

scholarly journals Cyber-Attack Detection in DC Microgrids Based on Deep Machine Learning and Wavelet Singular Values Approach

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1914
Author(s):  
Moslem Dehghani ◽  
Taher Niknam ◽  
Mohammad Ghiasi ◽  
Navid Bayati ◽  
Mehdi Savaghebi
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Cyber Security ◽  
Public Transportation ◽  
Energy Resource ◽  
Singular Values ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Gray Wolf ◽  
Cyber Attack

Nowadays, the role of cyber-physical systems (CPSs) is of paramount importance in power system security since they are more vulnerable to different cyber-attacks. Detection of cyber-attacks on a direct current microgrid (DC-MG) has become a pivotal issue due to the increasing use of them in various electrical engineering applications, from renewable power generations to the distribution of electricity and power system of public transportation and subway electric network. In this study, a novel strategy was provided to diagnose possible false data injection attacks (FDIA) in DC-MGs to enhance the cyber-security of electrical systems. Accordingly, to diagnose cyber-attacks in DC-MG and to identify the FDIA to distributed energy resource (DER) unit, a new procedure of wavelet transform (WT) and singular value decomposition (SVD) based on deep machine learning was proposed. Additionally, this paper presents a developed selective ensemble deep learning (DL) approach using the gray wolf optimization (GWO) algorithm to identify the FDIA in DC-MG. In the first stage, in the paper, to gather sufficient data within the ordinary performance required for the training of the DL network, a DC-MG was operated and controlled with no FDIAs. In the information generation procedure, load changing was considered to have diagnosing datasets for cyber-attack and load variation schemes. The obtained simulation results were compared with the new Shallow model and Hilbert Huang Transform methods, and the results confirmed that the presented approach could more precisely and robustly identify multiple forms of FDIAs with more than 95% precision.

Cyber Attack Detection of I&C Systems in NPPS Based on Physical Process Data

2016 ◽  
Author(s):  
Jianghai Li ◽  
Xiaojin Huang
Keyword(s):  
Control Systems ◽  
Physical Process ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Information Networks ◽  
Cyber Attack ◽  
Process Data ◽  
Cyber Defense ◽  
Multiple Variables

The cyber security problem is posing new challenges to the current safety analysis of nuclear power plants. Historically, analogue control systems in the absence of interactive communications are immune to cyber-attacks; however, digital control systems with extensive interconnection of reprogrammable components are intensely vulnerable to cyber-attacks which shed light on the significance and urgency of the cyber security. The current cyber security approaches, which merely focus on information networks, have not given multi-faceted considerations to instrumentation and control (I&C) systems. The cyber-attack on I&C systems may lead to more severe consequences, including the abnormal change of parameters, the malfunction of equipment, and even the accident condition. The existing cyber security approaches for information networks, such as firewalls, encryption, can enhance the cyber security of I&C systems, but are often insufficient in addressing challenges associate with the I&C systems which link cyber space and physical systems. The defense approach based on physical information should be developed to meet the emerging challenges. In this paper, we propose the cyber-physical security (CPS) approach based on the physical process data for the cyber defense. This approach does not intend to replace current cyber defense mechanisms. It could be served as the last barrier for security defense. The goal of the CPS defense approach is to detect attacks at the beginning of the occurrence of physical process anomalies cause by cyber-attacks. A practical implementation of the CPS approach is proposed and its influence on the existing infrastructure is discussed. The statistical analysis techniques are utilized on physical process data for attack detection. The method of dynamic principal component analysis (dynamic PCA) is employed to characterize the correlation of multiple variables in the normal operational condition. In the abnormal operational occurrence, the chi-square detector is able to distinguish adversarial cyber-attacks from ordinary random failures.

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