[1] L. Atzori, A. Iera and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp.
2787–2805, 2010.
[2] S. Sicari, A. Rizzardi, L. A. Grieco and A. Coen-Porisini, “Security, privacy and trust in Internet of Things: The roadahead,” Computer Networks, vol. 76, pp. 146–164, 2015.
[3] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021.
[4] J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and
future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
[5] L. Atzori, A. Iera and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp.
2787–2805, 2010.
[6] J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and
future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
[7] A. Botta, W. De Donato, V. Persico and A. Pescapé, “Integration of cloud computing and Internet of Things: A
survey,” Future Generation Computer Systems, vol. 56, pp. 684–700, 2016.
[8] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science, vol. 349, no.
6245, pp. 255–260, 2015.
[9] M. A. Ferrag, L. Maglaras, S. Moschoyiannis and H. Janicke, “Deep learning for cyber security intrusion detection,”
IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1347–1380, 2020.
[10] A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cybersecurity intrusion
detection,” IEEE Communications Surveys &Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.
[11] B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern
Recognition, vol. 84, pp. 317–331, 2018.
[12] R. Vinayakumaret al., “Deep learning approach for intelligent intrusion detection system,” IEEE Access, vol. 7,
pp. 41525–41550, 2019.
[13] M. Liyanage, A. Braeken, P. Kumar and M. Ylianttila, “IoT security: Advances in authentication,” Future
Generation Computer Systems, vol. 82, pp. 666–677, 2018.
[14] P. W. Singer and A. Friedman, Cybersecurity and Cyberwar: What Everyone Needs to Know. Oxford
University Press, 2014.
[15] R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,”
in Proc. IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
[16] A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cybersecurity intrusion
detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.
[17] C. Kolias, G. Kambourakis, A. Stavrou and J. Voas, “DDoS in the IoT: Mirai and other botnets,” Computer, vol.
50, no. 7, pp. 80–84, 2017.
[18] A. Botta, W. De Donato, V. Persico and A. Pescapé, “Integration of cloud computing and Internet of Things: A
survey,” Future Generation Computer Systems, vol. 56, pp. 684–700, 2016.
[19] B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern
Recognition, vol. 84, pp. 317–331, 2018.
[20] I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Computer
Science, vol. 2, no. 3, pp. 1–21, 2021.
[21] J. A. Kroll et al., “Accountable algorithms,” University of Pennsylvania Law Review, vol. 165, no. 3, pp. 633
705, 2017.
[22] Digital Forensic Research Workshop (DFRWS), “A roadmap for digital forensic research,” 2001.