Fetching the paper…
Reading the bibliography…
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS).
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF: identifying density-based local outliers,” in Proc. of SIGMOD , 2000, pp. 1–12
2000
Earlier work this paper cites.
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high-dimensional distribution,” Neural Computation , vol. 13, no. 7, pp. 1443–1471, 2001
2001
Earlier work this paper cites.
M. W. Hofbaur and B. C. Williams, “Mode estimation of probabilistic hybrid systems,” in Proc. of HSCC , 2002, pp. 253–266
2002
Earlier work this paper cites.
T. G. Dietterich, “Machine learning for sequential data: a review,” in Proc. of SSPR . Springer Berlin Heidelberg, 2002, pp. 15–30
2002
Earlier work this paper cites.
V. Verma, G. Gordon, R. Simmons, and S. Thrun, “Real-time fault diagnosis,” IEEE Robotics and Automation Magazine , vol. 11, no. 2, pp. 56–66, 2004
2004
Earlier work this paper cites.
M. W. Hofbaur and B. C. Williams, “Hybrid estimation of complex systems.” IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics , vol. 34, no. 5, pp. 2178–2191, 2004
2004
Earlier work this paper cites.
F. Zhao, X. Koutsoukos, H. Haussecker, J. Reich, and P. Cheung, “Monitoring and fault diagnosis of hybrid systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , vol. 35, no. 6, pp. 1225–1240, 2005
2005
Earlier work this paper cites.
S. Narasimhan and G. Biswas, “Model-based diagnosis of hybrid systems,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans , vol. 37, no. 3, pp. 348–361, 2007
2007
Earlier work this paper cites.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM computing surveys , 2009
2009
Earlier work this paper cites.
F. Pasqualetti, F. Dorfler, and F. Bullo, “Cyber-physical attacks in power networks: Models, fundamental limitations and monitor design,” in Proc. of CDC , 2011, pp. 2195–2201
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011
2011
Cited alongside, same era.
C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology , vol. 2, pp. 27:1–27:27, 2011, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
2011
Cited alongside, same era.
A. Teixeira, D. Pérez, H. Sandberg, and K. H. Johansson, “Attack models and scenarios for networked control systems,” in Proc. of HiCoNS , 2012, p. 55
2012
Cited alongside, same era.
J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research , vol. 13, no. Feb, pp. 281–305, 2012
2012
Cited alongside, same era.
Y. Chen, C. M. Poskitt, and J. Sun, “Towards learning and verifying invariants of cyber-physical systems by code mutation,” in Proc. of FM , 2016, pp. 155–163
2016
Later among the works it cites.
S. Zhai, Y. Cheng, W. Lu, and Z. Zhang, “Deep structured energy based models for anomaly detection,” in Proc. of ICML , vol. 48, 2016
2016
Later among the works it cites.
H. R. Ghaeini and N. O. Tippenhauer, “HAMIDS: hierarchical monitoring intrusion detection system for industrial control systems,” in Proc. of CPS-SPC . ACM, 2016, pp. 103–111
2016
Later among the works it cites.
S. Adepu and A. Mathur, “Using process invariants to detect cyber attacks on a water treatment system,” in Proc. of SEC , ser. IFIP AICT, vol. 471. Springer, 2016, pp. 91–104
2016
Later among the works it cites.
——, “Distributed detection of single-stage multipoint cyber attacks in a water treatment plant,” in Proc. of AsiaCCS . ACM, 2016, pp. 449–460
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Jones, Z. Kong, and C. Belta, “Anomaly detection in cyber-physical systems: A formal methods approach,” in Proc. of CDC , 2014, pp. 848–853
2014
Cited alongside, same era.
C. Aggarwal, Outlier analysis . Springer Publishing Company, 2015
2015
Cited alongside, same era.
P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long short term memory networks for anomaly detection in time series,” in Proc. of ESANN , 2015, p. 89
2015
Cited alongside, same era.
S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a next-generation open source framework for deep learning,” in Proceedings of LearningSys , vol. 5, 2015
2015
Cited alongside, same era.
J. Goh, S. Adepu, K. N. Junejo, and A. Mathur, “A dataset to support research in the design of secure water treatment systems,” in Proc. of CRITIS , 2016
2016
Cited alongside, same era.
2016
Later among the works it cites.
Y. Harada, Y. Yamagata, O. Mizuno, and E.-H. Choi, “Log-based anomaly detection of CPS using a statistical method,” in Proc. of IWESEP . IEEE, 2017, pp. 1–6
2017
Closest in time.
“Secure Water Treatment (SWaT),” http://itrust.sutd.edu.sg/research/testbeds/secure-water-treatment-swat/
2017
Closest in time.
J. Goh, S. Adepu, M. Tan, and Z. S. Lee, “Anomaly detection in cyber physical systems using recurrent neural networks,” in Proc. of HASE . IEEE, 2017, pp. 140–145
2017
Closest in time.
“SWaT dataset and models,” https://itrust.sutd.edu.sg/dataset/
2017
Closest in time.