Autonomous Threat Intelligence Systems for Dynamic Cyber Defense
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Abstract
Bustling city streets are filled with the sound of a tiger; the sound of your footsteps as you walk through them leaks into an orchestra that neither has harmony nor rhythm. Lights from sky-scrapers, which are huge and high in the sky at night, pierce into their night's black expanse, sending a strange light cascading around the metropolis. Swarms of people flow through the gaps between skyscrapers, looking like lone figures standing at the hub of an otherwise teeming world. Crowds line the streets, and the air hangs heavy with the smells from both exhaust gases and steaming pots of food being sizzled up, commingling it all. Yet--though the noise is deafening and never-ending--there is still an inherent energy which runs through the city, a non-stop heartbeat that sends it forward into the night.
Bustling city streets are filled with the sound of a tiger; the sound of your footsteps as you walk through them leaks into an orchestra that neither has harmony nor rhythm. Lights from sky-scrapers, which are huge and high in the sky at night, pierce into their night's black expanse, sending a strange light cascading around the metropolis. Swarms of people flow through the gaps between skyscrapers, looking like lone figures standing at the hub of an otherwise teeming world. Crowds line the streets, and the air hangs heavy with the smells from both exhaust gases and steaming pots of food being sizzled up, commingling it all. Yet--though the noise is deafening and never-ending--there is still an inherent energy which runs through the city, a non-stop heartbeat that sends it forward into the night.
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[1] A. Milenkoski, M. Vieira, S. Kounev, A. Avritzer, and B. D. Payne, "Evaluating computer intrusion detection systems: A survey of common practices," ACM Computing Surveys, vol. 48, no. 1, pp. 1–41, 2015.
[2] R. Mitchell and I. R. Chen, "A survey of intrusion detection techniques for cyber-physical systems," ACM Computing Surveys, vol. 46, no. 4, pp. 1–29, 2014.
[3] A. L. Buczak and E. Guven, "A survey of data mining and machine learning methods for cyber security intrusion detection," IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.
[4] D. E. Denning, "An intrusion-detection model," IEEE Transactions on Software Engineering, vol. SE-13, no. 2, pp. 222–232, 1987.
[5] C.-F. Tsai, Y.-F. Hsu, C.-Y. Lin, and W.-Y. Lin, "Intrusion detection by machine learning: A review," Expert Systems with Applications, vol. 36, no. 10, pp. 11994–12000, 2009.
[6] S. Mukkamala, G. Janoski, and A. Sung, "Intrusion detection using neural networks and support vector machines," in Proc. IEEE International Joint Conference on Neural Networks, 2002, pp. 1702–1707.
[7] T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794.
[8] M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, "Network anomaly detection: Methods, systems and tools," IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303–336, 2014.
[9] R. Sommer and V. Paxson, "Outside the closed world: On using machine learning for network intrusion detection," in Proc. 31st IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
[10] L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[11] A. A. Diro and N. Chilamkurti, "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, vol. 82, pp. 761–768, 2018.
[12] J. Jacobs, S. Romanosky, B. Edwards, I. Adjerid, and M. Roytman, "Exploit prediction scoring system (EPSS)," arXiv preprint arXiv:1908.04856, 2019.
[13] S. Frei, T. Duebendorfer, G. Ollmann, and M. May, "Understanding the web browser threat: Examination of vulnerable online web browser populations and the insecurity iceberg," ETH Zurich, Tech. Rep., 2008.
[14] D. Bhatt, P. Patel, H. Talsania, J. Patel, R. Vaghela, S. Pandya, K. Modi, and H. Ghayvat, "CNN variants for computer vision: History, architecture, application, challenges and future scope," Electronics, vol. 10, no. 20, p. 2470, 2021.
[15] Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, "Kitsune: An ensemble of autoencoders for online network intrusion detection," in Proc. Network and Distributed System Security Symposium (NDSS), 2018.
[16]Rasul, I., Tohfa, N. A., Rahman, M., Hossain, I., Zareen, S., & Shakhawat, M. (2023). Quantum Machine Learning for Early Disease Diagnosis: A Systematic Review and Public Health Innovation Perspective. World Journal of Advanced Research and Reviews, 19(01), 1668-1674.
[17] Tohfa, N. A., Hossain, I., Zareen, S., Rasul, I., Hossen, M. S., & Rahman, M. (2021). Adversarial Cognition Machine Learning at the Frontlines of Cyber Warfare. World Journal of Advanced Research and Reviews, 12(02), 722-729.
[18] Alam, A., Gazi, M. S., Abdullah, S. M., Tasnim, M., Himeluzzaman, M., Nabil, M. A., ... & Akter, S. (2021). Quantum-resilient federated intrusion detection: A hybrid quantum classical framework for safeguarding US critical infrastructure in the post-quantum era. International Journal of Advances in Signal and Image Sciences, 7(1), 57-72.
[19]Biswas, B., Chaudhury, T. H., & Mohammad, S. N. A Fast and Scalable Recommender System using Collaborative Filtering Technique in Python Scikit-learn.
[20]Hossain, M. S., Biswas, B., & Rahaman, M. M. (2023). THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING CYBERSECURITY DEFENSE MECHANISMS. International Journal Of Engineering Technology Research & Management (IJETRM), 7(07), 157-166.