Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems
Tài liệu tham khảo
Ahmed, 2017, Wadi: A water distribution testbed for research in the design of secure cyber physical systems, 25
Ahmed, 2018, Noise matters: Using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in cps, 566
Bathelt, 2015, Revision of the tennessee eastman process model, IFAC-PapersOnLine, 48, 309, 10.1016/j.ifacol.2015.08.199
Biggio, 2013, Evasion attacks against machine learning at test time, 387
Biggio, 2018, Wild patterns: Ten years after the rise of adversarial machine learning, Pattern Recognition, 84, 317, 10.1016/j.patcog.2018.07.023
Bitton, 2021, Evaluating the cybersecurity risk of real world, machine learning production systems, arXiv preprint arXiv:2107.01806
Candell, 2015, An industrial control system cybersecurity performance testbed, National Institute of Standards and Technology. NISTIR, 8089
Demetrio, 2021, Functionality-preserving black-box optimization of adversarial windows malware, IEEE Transactions on Information Forensics and Security, 16, 3469, 10.1109/TIFS.2021.3082330
Demetrio, 2021, Adversarial EXEmples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection, ACM Trans. Priv. Secur., 24, 10.1145/3473039
Downs, 1993, A plant-wide industrial process control problem, Computers & chemical engineering, 17, 245, 10.1016/0098-1354(93)80018-I
Erba, 2020, Constrained concealment attacks against reconstruction-based anomaly detectors in industrial control systems, 480
Feng, 2017, A deep learning-based framework for conducting stealthy attacks in industrial control systems, arXiv preprint arXiv:1709.06397
Formby, 2018, Lowering the barriers to industrial control system security with {GRFICS}
Ghafouri, 2018, Adversarial regression for detecting attacks in cyber-physical systems, 3769
Giraldo, 2017, Security and privacy in cyber-physical systems: A survey of surveys, IEEE Design & Test, 34, 7, 10.1109/MDAT.2017.2709310
Giraldo, 2018, A survey of physics-based attack detection in cyber-physical systems, ACM Computing Surveys (CSUR), 51, 76
Goh, 2016, A dataset to support research in the design of secure water treatment systems, 88
Goh, 2017, Anomaly detection in cyber physical systems using recurrent neural networks, 140
Goodfellow, 2016, Vol. 1
Herzberg, 2019, The chatty-sensor: a provably-covert channel in cyber physical systems, 638
Huang, 2020, A dynamic games approach to proactive defense strategies against advanced persistent threats in cyber-physical systems, Computers & Security, 89, 101660, 10.1016/j.cose.2019.101660
Humayed, 2017, Cyber-physical systems security a survey, IEEE Internet of Things Journal, 4, 1802, 10.1109/JIOT.2017.2703172
Inoue, 2017, Anomaly detection for a water treatment system using unsupervised machine learning, 1058
Jia, 2021, Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems, International Journal of Critical Infrastructure Protection, 100452, 10.1016/j.ijcip.2021.100452
Kim, 2019, Anomaly detection for industrial control systems using sequence-to-sequence neural networks, 3
Kiss, 2015, Denial of service attack detection in case of tennessee eastman challenge process, Procedia Technology, 19, 835, 10.1016/j.protcy.2015.02.120
Kravchik, 2021, Poisoning attacks on cyber attack detectors for industrial control systems, 116
Kravchik, 2018, Detecting cyber attacks in industrial control systems using convolutional neural networks, 72
Kravchik, 2021, Efficient cyber attack detection in industrial control systems using lightweight neural networks and pca, IEEE Transactions on Dependable and Secure Computing
Krotofil, 2013, Resilience of process control systems to cyber-physical attacks, 166
Krotofil, 2015, Rocking the pocket book: Hacking chemical plants
Kushner, 2013, The real story of stuxnet, IEEE Spectrum, 3, 48, 10.1109/MSPEC.2013.6471059
Li, 2021, Conaml: Constrained adversarial machine learning for cyber-physical systems, 52
Lin, 2018, Tabor: a graphical model-based approach for anomaly detection in industrial control systems, 525
Liu, 2020, Toward security monitoring of industrial cyber-physical systems via hierarchically distributed intrusion detection, Expert Systems With Applications, 158, 113578, 10.1016/j.eswa.2020.113578
Maclaurin, 2015, Gradient-based hyperparameter optimization through reversible learning, 2113
Madani, 2018, Robustness of deep autoencoder in intrusion detection under adversarial contamination, 1
Mitchell, 2014, A survey of intrusion detection techniques for cyber-physical systems, ACM Computing Surveys, 46, 55, 10.1145/2542049
Muñoz-González, 2017, Towards poisoning of deep learning algorithms with back-gradient optimization, 27
Nedeljkovic, 2021, Cnn based method for the development of cyber-attacks detection algorithms in industrial control systems, Computers & Security, 102585
Noorizadeh, 2021, A cyber-security methodology for a cyber-physical industrial control system testbed, IEEE Access, 9, 16239, 10.1109/ACCESS.2021.3053135
Pechenizkiy, 2010, Online mass flow prediction in cfb boilers with explicit detection of sudden concept drift, ACM SIGKDD Explorations Newsletter, 11, 109, 10.1145/1809400.1809423
Pires, 2004, Malicious node detection in wireless sensor networks, 24
Raman, 2019, Anomaly detection in critical infrastructure using probabilistic neural network, 129
Ravikumar, 2020, Next-generation cps testbed-based grid exercise-synthetic grid, attack, and defense modeling, 92
Rosenberg, 2018, Generic black-box end-to-end attack against state of the art api call based malware classifiers, 490
Rubinstein, 2009, Antidote: understanding and defending against poisoning of anomaly detectors, 1
Shafahi, 2018, Poison frogs! targeted clean-label poisoning attacks on neural networks, 6103
Shi, 2004, Designing secure sensor networks, IEEE Wireless Communications, 11, 38, 10.1109/MWC.2004.1368895
Suciu, 2018, When does machine learning fail? generalized transferability for evasion and poisoning attacks, 1299
Szegedy, 2014, Intriguing properties of neural networks
Taormina, 2018, Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems, Journal of Water Resources Planning and Management, 144, 04018065, 10.1061/(ASCE)WR.1943-5452.0000983
Taormina, 2018, Battle of the attack detection algorithms: Disclosing cyber attacks on water distribution networks, Journal of Water Resources Planning and Management, 144, 04018048, 10.1061/(ASCE)WR.1943-5452.0000969
Yin, 2012, A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process, Journal of process control, 22, 1567, 10.1016/j.jprocont.2012.06.009
Zizzo, 2019, Adversarial machine learning beyond the image domain, 1
Zizzo, 2020, Adversarial attacks on time-series intrusion detection for industrial control systems, 899