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Solar Power Plant Network Packet-Based Anomaly Detection System for cybersecurity

Computers, Materials & Continua 2023年第10期77卷 757-779页

作者： Ju Hyeon Lee Jiho Shin Jung Taek Seo Department of Information Security Gachon UniversitySeongnam13120Korea Police Science Institute Korean National Police UniversityAsan31539Korea Department of Computer Engineering Gachon UniversitySeongnam13120Korea

As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent *** energy is becoming increasingly important,with solar power accounting for the most significant proportion of *** the scale and importance of solar energy have increased,cyber threats against solar power plants have also ***,we need an anomaly detection system that effectively detects cyber threats to solar power ***,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect *** address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber *** and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power *** analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant *** develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such *** Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.

关键词： Renewable energy solar power plant cyber threat cybersecurity anomaly detection machine learning network packet

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Active resilient defense control against false data injection attacks in smart grids

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Control Theory and Technology 2023年第4期21卷 515-529页

作者： Xiaoyuan Luo Lingjie Hou Xinyu Wang Ruiyang Gao Shuzheng Wang Xinping Guan School of Electrical Engineering Yanshan UniversityQinhuangdao066004HebeiChina School of Electrical Engineering Jiangsu Collaborative Innovation Center for Smart Distribution NetworkNanjing636600JiangsuChina School of Electronic and Electric Engineering Shanghai Jiao Tong UniversityShanghai200240China

The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart *** this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart *** proposed active defense highlights the integration of detection and defense against FDIAs in smart ***,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into ***,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put *** to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by *** matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)*** system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain *** active resilient defense approach can be built in real time and show accurate and quick respond to the injected *** effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.

关键词： Active resilient defense Attack detection cyber attacks cyber-attack detection cyber grid elements cyber threat False data injection attack Smart grids security Interval observer

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cybersecurity and cyber Forensics: Machine Learning Approach Systematic Review

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Semiconductor Science and Information Devices 2020年第2期2卷 25-29页

作者： Ibrahim Goni Jerome MGumpy Timothy UMaigari Murtala Mohammad Department of Computer Science Adamawa State UniversityMubiNigeria Department of Computer Science Federal UniversityGashuaNigeria Department of Computer Science Federal College of Education GombeNigeria

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations(developed and developing countries)worldwide in which global network provide platform for the *** forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which is perfectly enough for the court of law to use and make a judgment based on the comprehensiveness,authenticity and objectivity of the information *** is of major concerned to the internet users worldwide due to the recent form of attacks,threat,viruses,intrusion among others going on every day among internet of ***,it is noted that cybersecurity is based on confidentiality,integrity and validity of *** aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics and pave away for further research directions on the application of deep learning,computational intelligence,soft computing to cybersecurity and cyber forensics.

关键词： cybersecurity cyber forensics cyber space cyber threat Machine learning and deep learning

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