Abstract
As the digital landscape continues to expand, the complexity and frequency of cyber threats targeting critical information systems also increases. Effective cyber threat detection has become a paramount concern for safeguarding sensitive data and ensuring the uninterrupted operation of various infrastructures. This research introduces a novel approach to cyber threat detection through the design of a greedy algorithm tailored for identifying specific types of threats. The algorithm focuses on a simplified aspect of threat detection, aiming to highlight the potential of greedy algorithms in contributing to the broader field of cybersecurity. The methodology involves monitoring network traffic for signs of port scanning activity, a common precursor to potential cyber-attacks. The algorithm's effectiveness is evaluated in terms of its ability to accurately identify suspicious scanning behavior while minimizing false positives. By presenting this algorithmic framework, the research aims to contribute to the ongoing efforts in enhancing cyber threat detection techniques.
References
- Reddy, Dukka Karun Kumar, et al. "Exact greedy algorithm based split finding approach for intrusion detection in fog-enabled IoT environment." Journal of Information Security and Applications 60 (2021): 102866.
- Schlenker, Aaron, et al. "Deceiving cyber adversaries: A game theoretic approach." AAMAS'18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. IFAAMAS, 2018.
- Elderman, Richard, et al. "Adversarial reinforcement learning in a cyber-security simulation." 9th International Conference on Agents and Artificial Intelligence (ICAART 2017). SciTePress Digital Library, 2017.
- Delplace, Antoine, Sheryl Hermoso, and Kristofer Anandita. "Cyber-attack detection thanks to machine learning algorithms." arXiv preprint arXiv:2001.06309 (2020).
- Salih, Azar, et al. "A survey on the role of artificial intelligence, machine learning and deep learning for cybersecurity attack detection." 2021 7th International Engineering Conference “Research & Innovation amid Global Pandemic"(IEC). IEEE, 2021.
- Kurt, Mehmet Necip, et al. "Online cyber-attack detection in smart grid: A reinforcement learning approach." IEEE Transactions on Smart Grid 10.5 (2018): 5174-5185.
- Kumar, Sunil, Bhanu Pratap Singh, and Vinesh Kumar. "A Semantic Machine Learning Algorithm for Cyber Threat Detection and Monitoring Security." 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021.
- Chalé, Marc, Nathaniel D. Bastian, and Jeffery Weir. "Algorithm selection framework for cyber attack detection." Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning. 2020.
- Alqahtani, Hamed, et al. "Cyber intrusion detection using machine learning classification techniques." Computing Science, Communication and Security: First International Conference, COMS2 2020, Gujarat, India, March 26–27, 2020, Revised Selected Papers 1. Springer Singapore, 2020.
- Kumar, Sunil, Bhanu Pratap Singh, and Vinesh Kumar. "A Semantic Machine Learning Algorithm for Cyber Threat Detection and Monitoring Security." 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021.
- Lee, Jonghoon, et al. "Cyber threat detection based on artificial neural networks using event profiles." Ieee Access 7 (2019): 165607-165626.
