Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems

By Shengyi Pan, Thomas H. Morris, Uttam Adhikari. IEEE Transactions on Smart Grid, 2015

399 citations recorded by the source corpus.

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A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks (2019, background): “[34] proposed a systematic and automated approach to build a hybrid IDS that learns temporal statebased specifications for electric power systems to accurately differentiate between disturbances, normal control operations, and cyber-attacks.”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, methodology): “These datasets can be used for cyber security intrusion detection in the industrial control systems [86], [134], [127], [135], [128].”
Security of smart manufacturing systems (2018, methodology): “Power transmission system [88] Behaviour Network Fault replay, command injection, zero-day Simulation (synchrophasor measurement data and”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, methodology): “Other notable techniques used for this cause are Bayesian framework, particle swarm optimization (PSO), adaboost, random forests, and common path mining method [200], [251]–[253].”
A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, methodology): “Common path mining (CPM) was applied in detecting FDIA in [25], [125].”