2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 5 , Issue 1 , PP: 33-48, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Osama Maher 1 * , Elena Sitnikova 2
Doi: https://doi.org/10.54216/JISIoT.050104
Since the Industrial Internet of Things (IIoT) networks comprise heterogeneous manufacturing and technological devices and services, discovering advanced cyber threats is an arduous and risk-prone process. Cyber-attack detection techniques have been recently emerged to understand the process of obtaining knowledge about cyber threats to collect evidence. These techniques have broadly employed for identifying malicious events of cyber threats to protect organizations’ assets. The main limitation of these systems is that they are not able to discover and interpret new attack activities. This paper proposes a new adversarial deep learning for discovering adversarial attacks in IIoT networks. Evaluation of correlation reduction has been used as a means of feature selection for reducing the impact of data poisoning attacks on the subsequent deep learning techniques. Feed Forward Deep Neural Networks have been developed using across various parameter permutations, at differing rates of data poisoning, to develop a robust deep learning architecture. The results of the proposed technique have been compared with previously developed deep learning models, proving the increased robustness of the new deep learning architectures across the ToN_IoT datasets.
Adversarial deep learning , adversarial attacks , Industrial Internet of Things
[1] J. Cai, J. Luo, S. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing, vol. 300, pp. 70–79, 2018.
[2] Y. Zhao, J. Chen, J. Zhang, D. Wu, M. Blumenstein, and S. Yu, “Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks,” Concurrency and Computation: Practice and Experience, p. e5906, 2020.
[3] E. Alhajjar, P. Maxwell, and N. D. Bastian, “Adversarial machine learning in network intrusion detection systems,” arXiv preprint arXiv:2004.11898, 2020.
[4] A. Agarwal and A. Agarwal, “The security risks associated with cloud computing,” International Journal of Computer Applications in Engineering Sciences, vol. 1, pp. 257–259, 2011.
[5] Y. Vorobeychik, Adversarial machine learning. [San Rafael, California]: San Rafael, California : Morgan & Claypool, 2018.
[6] S. Leminen, M. Westerlund, M. Rajahonka, and R. Siuruainen, “Towards iot ecosystems and business models,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7469, 2012, pp. 15–26.
[7] A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” arXiv preprint arXiv:1611.01236, 2016.
[8] B. S. Vivek, K. R. Mopuri, and R. V. Babu, “Gray-box adversarial training,” in Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
[9] N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 IEEE symposium on security and privacy (sp). IEEE, 2017, Conference Proceedings, pp. 39–57.
[10] V. Duddu, “A survey of adversarial machine learning in cyber warfare,” Defence Science Journal, vol. 68, no. 4, 2018.
[11] M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar, “Can machine learning be secure?” in Proceedings of the 2006 ACM Symposium on Information, computer and communications security, 2006, Conference Proceedings, pp. 16–25.
[12] P. McDaniel, N. Papernot, and Z. B. Celik, “Machine learning in adversarial settings,” IEEE Security & Privacy, vol. 14, no. 3, pp. 68–72, 2016.
[13] L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. D. Tygar, “Adversarial machine learning,” in Proceedings of the 4th ACM workshop on Security and artificial intelligence, 2011, Conference Proceedings, pp. 43–58.
[14] M. Zamani and M. Movahedi, “Machine learning techniques for intrusion detection,” arXiv preprint arXiv:1312.2177, 2013.
[15] M. F. Elrawy, A. I. Awad, and H. F. Hamed, “Intrusion detection systems for iot-based smart environments: a survey,” Journal of Cloud Computing, vol. 7, no. 1, p. 21, 2018.
[16] B. Zarpelao, R. Miani, C. T. Kawakani, and S. C. de Alvarenga, “A survey of intrusion detection in internet of things,” J. Netw. Comput. Appl., vol. 84, pp. 25–37, 2017.
[17] J. Steinhardt, P. W. W. Koh, and P. S. Liang, “Certified defenses for data poisoning attacks,” in Advances in neural information processing systems, 2017, Conference Proceedings, pp. 3517–3529.
[18] B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” arXiv preprint arXiv:1206.6389, 2012.
[19] B. Biggio, K. Rieck, D. Ariu, C. Wressnegger, I. Corona, G. Giacinto, and F. Roli, “Poisoning behavioral malware clustering,” in Proceedings of the 2014 workshop on artificial intelligent and security workshop, 2014, Conference Proceedings, pp. 27–36.
[20] B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph, B. I. Rubinstein, U. Saini, C. Sutton, J. Tygar, and K. Xia, Misleading learners: Co-opting your spam filter. Springer, 2009, pp. 17–51.
[21] J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, “Feature selection: A data perspective,” ACM Computing Surveys (CSUR), vol. 50, no. 6, pp. 1–45, 2017.
[22] M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for internet of things (iot) security,” IEEE Communications Surveys & Tutorials, 2020.
[23] N. Gogtay and U. Thatte, “Principles of correlation analysis,” Journal of the Association of Physicians of India, vol. 65, no. 3, pp. 78–81, 2017. [24] R. Rivera, C. M. Pérez, M. N. MartÃnez, and E. H. Suárez, “Correlation analysis,” in Applications of Regression Models in Epidemiology. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2017, pp. 87–96.
[25] C. Obite, N. Olewuezi, G. Ugwuanyim, and D. Bartholomew, “Multicollinearity effect in regression analysis: A feed forward artificial neural network approach,” Asian Journal of Probability and Statistics, pp. 22– 33, 2020.
[26] A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on explainable artificial intelligence (xai),” IEEE Access, vol. 6, pp. 52138– 52160, 2018.
[27] D. Janzing, L. Minorics, and P. Blöbaum, “Feature relevance quantification in explainable ai: A causal problem,” in International Conference on Artificial Intelligence and Statistics. PMLR, 2020, pp. 2907–2916.
[28] A. C. Ian Goodfellow, Yoshua Bengio, Deep Learning. MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
[29] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 308–318.
[30] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, vol. 15, no. 1, pp. 1929–1958, 2014.
[31] W. M. Van der Aalst, V. Rubin, H. Verbeek, B. F. van Dongen, E. Kindler, and C. W. Günther, “Process mining: a two-step approach to balance between underfitting and overfitting,” Software & Systems Modeling, vol. 9, no. 1, p. 87, 2010.
[32] N. Moustafa, “Ton_iot datasets,” https://cloudstor.aarnet.edu.au/plus/s/ ds5zW91vdgjEj9i, 2019.
[33] N. Moustafa, G. Creech, and J. Slay, Anomaly detection system using beta mixture models and outlier detection. Springer, 2018, pp. 125–135. [34] A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood, and A. Anwar, “Ton_iot telemetry dataset: A new generation dataset of iot and iiot for data-driven intrusion detection systems,” IEEE Access, vol. 8, pp. 165130–165150, 2020.
[35] H. G. Kayacik, A. N. Zincir-Heywood, and M. I. Heywood, “Selecting features for intrusion detection: A feature relevance analysis on kdd 99 intrusion detection datasets,” in Proceedings of the third annual conference on privacy, security and trust, vol. 94. Citeseer, 2005, pp. 1723–1722.
