A behavioral-based forensic investigation approach for analyzing attacks on water plants using GANs

[1]  MARIAM ELNOUR,et al.  A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems , 2020, IEEE Access.

[2]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[3]  Bruno Sinopoli,et al.  Cyber Meets Control: A Novel Federated Approach for Resilient CPS Leveraging Real Cyber Threat Intelligence , 2017, IEEE Communications Magazine.

[4]  Florian Dörfler,et al.  Attack Detection and Identification in Cyber-Physical Systems -- Part II: Centralized and Distributed Monitor Design , 2012, ArXiv.

[5]  Yang Hong,et al.  A GAN-Based Anomaly Detection Approach for Imbalanced Industrial Time Series , 2019, IEEE Access.

[6]  Mourad Debbabi,et al.  Behavioral analytics for inferring large-scale orchestrated probing events , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[7]  Jun Sun,et al.  Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[8]  Bruno Sinopoli,et al.  Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs , 2015, IEEE Control Systems.

[9]  Jun Li,et al.  One-Class Adversarial Nets for Fraud Detection , 2018, AAAI.

[10]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[11]  L. Shapley.  A Value for n-person Games , 1988 .

[12]  Jill Slay,et al.  Lessons Learned from the Maroochy Water Breach , 2007, Critical Infrastructure Protection.

[13]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[14]  Sridhar Adepu,et al.  A Dataset to Support Research in the Design of Secure Water Treatment Systems , 2016, CRITIS.

[15]  Nasir Ghani,et al.  Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations , 2019, IEEE Communications Surveys & Tutorials.

[16]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[17]  Qin Lin,et al.  TABOR: A Graphical Model-based Approach for Anomaly Detection in Industrial Control Systems , 2018, AsiaCCS.

[18]  Bruno Sinopoli,et al.  Detecting Integrity Attacks on SCADA Systems , 2011 .

[19]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[20]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[21]  Shi Wen-kang,et al.  Combining belief functions based on distance of evidence , 2004 .

[22]  Milos Manic,et al.  Toward Explainable Deep Neural Network Based Anomaly Detection , 2018, 2018 11th International Conference on Human System Interaction (HSI).

[23]  Rahul Khanna,et al.  Control theoretic approach to intrusion detection using a distributed hidden Markov model , 2008, IEEE Wireless Communications.

[24]  Avi Ostfeld,et al.  Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks , 2018, Journal of Water Resources Planning and Management.

[25]  Borko Furht,et al.  A survey of methods supporting cyber situational awareness in the context of smart cities , 2020, J. Big Data.

[26]  Ioana Giurgiu,et al.  Additive Explanations for Anomalies Detected from Multivariate Temporal Data , 2019, CIKM.

[27]  Lei Shi,et al.  MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks , 2019, ICANN.

[28]  Nicu Sebe,et al.  Abnormal event detection in videos using generative adversarial nets , 2017, 2017 IEEE International Conference on Image Processing (ICIP).