Abstract
Machine learning has become an imperative innovation for cybersecurity. It preemptively gets rid of digital dangers and supports security foundation utilizing different methods. Machine learning, a branch of artificial intelligence, utilizes formulas constructed from historical databases and observable analysis to create presumptions regarding the actions of a machine. The machine would then be able to alter its activities—and even perform capacities for which it hasn’t been unequivocally modified. With its capacity to figure out a huge number of records and distinguish conceivably risky ones, machine learning is progressively being utilized to reveal dangers and naturally squash them before they can unleash ruin. Looking at the numerous benefits, this chapter tries to explore the various forms of cyberattacks and the application of Machine Learning in handling these attacks and thereby increasing cyber security. The chapter then evaluates the various techniques of Machine Learning and how organizations could take advantage of these techniques. In the last, it addresses Machine Learning's potential opportunities for cyber security.
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References
Afanasyev, A., Mahadevan, P., Moiseenko, I., Uzun, E., & Zhang, L. (2013) Interest flooding attack and countermeasures in named data networking. In IFIP Networking Conference (pp. 1–9). IEEE.
Ar, A., Oktu, S. F., & Yaln, S. B,: Internet-of-things security: Denial of service attacks. In 2015 23nd Signal Processing and Communications Applications Conference (SIU) (pp. 903–906), May 2015.
Arbaugh, W. A., Farber, D. J., & Smith, J. M. (1997). A secure and reliable bootstrap architecture. In Proceedings. 1997 IEEE Symposium on Security and Privacy (Cat. No.97CB36097) (pp. 65–71), May 1997.
Abdallah, A., & Shen, X. S. (2016). Efficient prevention technique for false data injection attack in smart grid. In 2016 IEEE International Conference on Communications (ICC) (pp. 1–6), May 2016.
Chen, Y., Kar, S., & Moura, J. M. (2015). Cyber-physical systems: Dynamic sensor attacks and strong observability. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1752–1756). IEEE.
Chen, Y., Kar, S., & Moura, J. M. (2017). Dynamic attack detection in cyber-physical systems with side initial state information. IEEE Transactions on Automatic Control, 62(9), 4618–4624.
Dacosta, I., Ahamad, M., Traynor, P. (2012). Trust no one else: Detecting mitm attacks against ssl/tls without third-parties. In European Symposium on Research in Computer Security (pp. 199–216). Springer.
de Sa, A., Carmo, L., & Machado, R. (2017). Covert attacks in cyber-physical control systems. IEEE Transactions on Industrial Informatics.
Hahn, A., Thomas, R. K., Lozano, I., & Cardenas, A. (2015). A multi-layered and kill-chain based security analysis framework for cyber-physical systems. International Journal of Critical Infrastructure Protection, 11, 39–50.
https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/.
https://www.recordedfuture.com/machine-learning-cybersecurity-applications/.
https://apiumhub.com/tech-blog-barcelona/applications-machine-learning-cyber-security/.
Kohavi, R., Provost, F. (1998). Glossary of terms. Machine learning—special issue on applications of machine learning and the knowledge discovery process. Machine Learning, 30, 271–274.
Laso, P. M., Brosset, D., & Puentes, J. (2017). Dataset of anomalies and malicious acts in a cyber-physical subsystem. Data in Brief, 14, 186.
Lei, L., Wang, Y., Zhou, J., Wang, L., Zhang, Z. (2013). A threat to mobile cyber-physical systems: Sensor-based privacy theft attacks on android smartphones. In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (pp. 126–133). IEEE.
Min, B., & Varadharajan, V. (2015). Design and evaluation of feature dis- tributed malware attacks against the internet of things (IoT). In 2015 20th International Conference on Engineering of Complex Computer Systems (ICECCS) (pp. 80–89), Dec 2015.
Min, B., & Varadharajan, V. (2014). Design and analysis of a new feature- distributed malware. In 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 457–464), Sept 2014.
Mo, Y., Garone, E., Casavola, A., & Sinopoli, B. (2010). False data injection attacks against state estimation in wireless sensor networks. In 49th IEEE Conference on Decision and Control (CDC), (pp. 5967–5972), Dec 2010.
Nasser, O., Al Thuhli, S., Mohammed, M., Al Mamari, R., & Hajamo-Hideen, F. (2015). An investigation of backdoors implication to avoid regional security impediment. In 2015 Global Conference on Communication Technologies (GCCT) (pp. 409–412), April 2015
Nur, A. Y., & Tozal, M. E. (2016). Defending cyber-physical systems against dos attacks. In 2016 IEEE International Conference on Smart Comput- ing (SMARTCOMP) (pp. 1–3), May 2016.
Orojloo, H., & Azgomi, M. A. (2017). A game-theoretic approach to model and quantify the security of cyber-physical systems. Computers in Industry, 88, 44–57.
Pan, L., Zheng, X., Chen, H., Luan, T., Bootwala, H., & Batten, L. (2017). Cyber security attacks to modern vehicular systems. Journal of Information Security and Applications, 36, 90–100.
Parno, B. (2008). Bootstrapping trust in a “trusted platform.” In HotSec.
Parno, B., McCune, J. M., & Perrig, A. (2010). Bootstrapping trust in com- modity computers. In 2010 IEEE Symposium on Security and Privacy (pp. 414–429), May 2010.
Pasqualetti, F., Drfler, F., & Bullo, F. (2013). Attack detection and identification in cyber- physical systems. IEEE Transactions on Automatic Control, 58(11), 2715–2729.
Poturalski, M., Flury, M., Papadimitratos, P., Hubaux, J.-P., & Le Boudec, J.-Y. (2010). The cicada attack: degradation and denial of service in ir ranging. In 2010 IEEE International Conference on Ultra-Wideband (ICUWB) (Vol. 2., pp. 1-4). IEEE.
Puangpronpitag, S., & Masusai, N. (2009). An efficient and feasible solution to ARP spoof problem. In 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2009. ECTI-CON 2009 (Vol. 2, pp. 910–913). IEEE.
Sabaliauskaite, G., & Mathur, A. P. Countermeasures to enhance cyber- physical system security and safety. In 2014 IEEE 38th International Computer Software and Applications Conference Workshops (pp. 13–18), July 2014.
Sabaliauskaite, G., & Mathur, A. P. (2013). Intelligent checkers to improve attack detection in cyber physical systems. In 2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (pp. 27–30), Oct 2013.
Tehranipoor, M., & Koushanfar, F. (2010). A survey of hardware trojan taxonomy and detection. IEEE Design Test of Computers, 27(1), 10–25.
Yuan, Y., & Mo, Y. (2015). Security in cyber-physical systems: Controller design against known-plaintext attack. In 2015 54th IEEE Conference on Decision and Control (CDC) (pp. 5814–5819), Dec 2015.
Wei, J., & Mendis, G. J. (2016). A deep learning-based cyber-physical strategy to mitigate false data injection attack in smart grids. In 2016 Joint Workshop on Cyber- Physical Security and Resilience in Smart Grids (CPSR-SG) (pp. 1–6), April 2016.
Wurm, J., Hoang, K., Arias, O., Sadeghi, A. R., & Jin, Y. (2016a). Security analysis on consumer and industrial iot devices. In: 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC) (pp. 519–524), Jan 2016.
Wurm, J., Jin, Y., Liu, Y., Hu, S., Heffner, K., Rahman, F., & Tehranipoor, M. (2016b) Introduction to cyber-physical system security: A cross-layer perspective.
Yampolskiy, M., Horvath, P., Koutsoukos, X. D., Xue, Y., & Szti-Panovits, J. (2013). Taxonomy for description of cross-domain attacks on CPS. In Proceedings of the 2nd ACM international conference on High confidence networked systems (pp. 135–142). ACM, 2013.
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Gupta, A., Gupta, R., Kukreja, G. (2021). Cyber Security Using Machine Learning: Techniques and Business Applications. In: Hamdan, A., Hassanien, A.E., Khamis, R., Alareeni, B., Razzaque, A., Awwad, B. (eds) Applications of Artificial Intelligence in Business, Education and Healthcare . Studies in Computational Intelligence, vol 954. Springer, Cham. https://doi.org/10.1007/978-3-030-72080-3_21
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