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 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 The usage of power system multi-model forecasting aided state estimation for cyber attack detection

2022 ◽  
Vol 23 (5) ◽  
pp. 13-23
Author(s):  
I. A. Lukicheva ◽  
A. L. Kulikov
Keyword(s):  
Power System ◽  
State Estimation ◽  
Estimation Algorithm ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Protective Measure ◽  
Cyber Attack ◽  
False Data Injection ◽  
Forecasting Models ◽  
Autoregression Model

THE PURPOSE. Smart electrical grids involve extensive use of information infrastructure. Such an aggregate cyber-physical system can be subject to cyber attacks. One of the ways to counter cyberattacks is state estimation. State Estimation is used to identify the present power system operating state and eliminating metering errors and corrupted data. In particular, when a real measurement is replaced by a false one by a malefactor or a failure in the functioning of communication channels occurs, it is possible to detect false data and restore them. However, there is a class of cyberattacks, so-called False Data Injection Attack, aimed at distorting the results of the state estimation. The aim of the research was to develop a state estimation algorithm, which is able to work in the presence of cyber-attack with high accuracy.METHODS. The authors propose a Multi-Model Forecasting-Aided State Estimation method based on multi-model discrete tracking parameter estimation by the Kalman filter. The multimodal state estimator consisted of three single state estimators, which produced single estimates using different forecasting models. In this paper only linear forecasting models were considered, such as autoregression model, vector autoregression model and Holt’s exponen tial smoothing. When we obtained the multi-model estimate as the weighted sum of the single-model estimates. Cyberattack detection was implemented through innovative and residual analysis. The analysis of the proposed algorithm performance was carried out by simulation modeling using the example of a IEEE 30-bus system in Matlab.RESULTS. The paper describes an false data injection cyber attack and its specific impact on power system state estimation. A Multi - Model Forecasting-Aided State Estimation algorithm has been developed, which allows detecting cyber attacks and recovering corrupted data. Simulation of the algorithm has been carried out and its efficiency has been proved.CONCLUSION. The results showed the cyber attack detection rate of 100%. The Multi-Model Forecasting-Aided State Estimation is an protective measure against the impact of cyber attacks on power system.

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 Hybrid Detection of Intermittent Cyber-Attacks in Networked Power Systems

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4625 ◽  
Author(s):  
Efstathios Kontouras ◽  
Anthony Tzes ◽  
Leonidas Dritsas
Keyword(s):  
Power System ◽  
Attack Detection ◽  
Cyber Attacks ◽  
The State ◽  
Load Frequency Control ◽  
Cyber Attack ◽  
Detection Mechanism ◽  
Data Corruption ◽  
Power Load ◽  
Hybrid Detection

This article addresses the concept of a compound attack detection mechanism, that links estimation-based and set-theoretic methods, and is mainly focused on the disclosure of intermittent data corruption cyber-attacks. The detection mechanism is developed as a security enhancing tool for the load-frequency control loop of a networked power system that consists of several interconnected control areas. The dynamics of the power network are derived in observable form in the discrete-time domain, considering that an adversary corrupts the frequency measurements of certain control areas by means of a bias injection cyber-attack. Simulations indicate that an estimation-based detector is unable to discern an intermittent attack, especially when the latter one occurs at the same time as changes in the power load. The detector can be improved by exploiting the safe operation constraints imposed on the state variables of the system. It is shown that the disclosure of intermittent data corruption cyber-attacks in the presence of unknown power load changes is guaranteed only when the estimation-based detector is combined with its set-theoretic counterpart. To this end, a robust invariant set for the networked power system is computed and an alarm is triggered whenever the state vector exits this set. Simulations indicate that the above detectors can operate jointly in terms of a hybrid scheme, which enhances their detection capabilities.

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.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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