Detection of power grid disturbances and cyber-attacks based on machine learning

Journal of Information Security and Applications - Tập 46 - Trang 42-52 - 2019
Defu Wang1, Xiaojuan Wang1, Yong Zhang1, Lei Jin1
1Beijing University of Posts and Telecommunications, Beijing, China

Tài liệu tham khảo

Kim, 2017, Cps (cyber physical system) based manufacturing system optimization, Procedia Comput Sci, 122, 518, 10.1016/j.procs.2017.11.401 Wei, 2017, Deis: Dependability engineering innovation for cyber-physical systems, 409 Irmak, 2018, An overview of cyber-attack vectors on scada systems, 1 Che, 2018, Cyber cascades screening considering the impacts of false data injection attacks, IEEE Trans Power Syst, 10.1109/TPWRS.2018.2827060 Case, 2016, Analysis of the cyber attack on the ukrainian power grid, Electric. Inform. Shar. Anal. Center (E-ISAC) Tomin, 2016, Machine learning techniques for power system security assessment, IFAC-PapersOnLine, 49, 445, 10.1016/j.ifacol.2016.10.773 Nader, 2014, Lp-norms in one-class classification for intrusion detection in scada systems., IEEE Trans Ind Informa, 10, 2308, 10.1109/TII.2014.2330796 Nader, 2014, Mahalanobis-based one-class classification, 1 Karnouskos, 2011, Stuxnet worm impact on industrial cyber-physical system security, 4490 Zhu, 2011, A taxonomy of cyber attacks on scada systems, 380 Giraldo, 2018, A survey of physics-based attack detection in cyber-physical systems, ACM Computing Surveys (CSUR), 51, 76, 10.1145/3203245 Ahmed, 2018, Noise matters: Using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in cps, 566, 10.1145/3274694.3274748 Shoukry, 2018, Smt-based observer design for cyber-physical systems under sensor attacks, ACM Transactions on Cyber-Physical Systems, 2, 5, 10.1145/3078621 Hadžiosmanović, 2014, Through the eye of the plc: Semantic security monitoring for industrial processes, 126, 10.1145/2664243.2664277 Pan, 2015, Developing a hybrid intrusion detection system using data mining for power systems, IEEE Trans Smart Grid, 6, 3104, 10.1109/TSG.2015.2409775 Ahmed, 2018, Noiseprint: Attack detection using sensor and process noise fingerprint in cyber physical systems, 483 Junejo, 2016, Behaviour-based attack detection and classification in cyber physical systems using machine learning, 34 Nader, 2016, Detection of cyberattacks in a water distribution system using machine learning techniques, 25 Maglaras, 2014, Intrusion detection in scada systems using machine learning techniques, 626 Hink, 2014, Machine learning for power system disturbance and cyber-attack discrimination, 1 Pan, 2015, Classification of disturbances and cyber-attacks in power systems using heterogeneous time-synchronized data, IEEE Trans Ind Inf, 11, 650, 10.1109/TII.2015.2420951 Keshk, 2017, Privacy preservation intrusion detection technique for scada systems, 1 Hastie, 2009, Multi-class adaboost, Stat Interface, 2, 349, 10.4310/SII.2009.v2.n3.a8 Giraldo, 2018, A survey of physics-based attack detection in cyber-physical systems, ACM Comput Surv, 51, 10.1145/3203245 Li, 2017, Feature selection: a data perspective, ACM Comput Surv, 50, 10.1145/3136625 Mohammadi, 2019, Cyber intrusion detection by combined feature selection algorithm, J Inf Secur Appl, 44, 80 Min, 2018, Tr-ids: anomaly-based intrusion detection through text-convolutional neural network and random forest, Secur Commun Netw, 2018, 10.1155/2018/4943509 Aceto, 2018, Multi-classification approaches for classifying mobile app traffic, J Netw Comput Appl, 103, 131, 10.1016/j.jnca.2017.11.007 Platt, 1998, 21 Fayyad, 1992, On the handling of continuous-valued attributes in decision tree generation, Mach Learn, 8, 87, 10.1007/BF00994007 Chen, 2016, Xgboost: A scalable tree boosting system, 785 Breiman, 2001, Random forests, Mach Learn, 45, 5, 10.1023/A:1010933404324 Genuer, 2017, Random forests for big data, Big Data Res, 9, 28, 10.1016/j.bdr.2017.07.003 Friedman, 2001, 1