Umesh Kisan Raut

Bio: Umesh Kisan Raut is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Intrusion detection system. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Log-Based Malicious Activity Detection Using Machine and Deep Learning

Abstract: This chapter describes the application of intelligent computational techniques to the problem of malicious activity detection. It is proposed to embed machine and deep learning models for malicious activity detection into the framework of a log-based decision support system (DSS) for information security administrators. It is expected that such a solution will enable organizational-wide protection of informational assets, by providing accurate and comprehensive real-time insights into violations of information security policies. In this work, we present experiments and results on database systems’ log analysis using traditional machine learning (ML) methods and deep learning (DL) on the synthetic log dataset simulating user activity in a hypothetical company.

Simulations of Event-Based Cyber Dynamics via Adversarial Machine Learning

Abstract: In this paper, we apply cybersecurity dynamics theory into practical scenarios. We use machine learning models as detection tools of intrusion detection systems and consider cyber attacks against node computers as well as adversarial attacks against machine learning models. We pay our attention to two problems. The first problem is when the network is attacked, how we can observe the states of the network and estimate its equilibrium with a lower cost. We apply an event-based observation and estimation method combined with machine learning-based intrusion detection systems. The second problem is to control the cost and the convergence speed of cybersecurity dynamics when it is under attack. An event-based control method and machine learning-based intrusion detection systems are put into use in this scenario. We simulate both scenarios and analyze the dynamics’ behaviors under an adversarial attack against the machine learning models on intrusion detection systems.