Biblio

Intrusion Detection Systems (IDS) are the systems that detect and block any potential threats (e.g. DDoS attacks) in the network. In this project, we explore the performance of several machine learning techniques when used as parts of an IDS. We experiment with the CICIDS2017 dataset, one of the biggest and most complete IDS datasets in terms of having a realistic background traffic and incorporating a variety of cyber attacks. The techniques we present are applicable to any IDS dataset and can be used as a basis for deploying a real time IDS in complex environments.

Multi-Agent Task Allocation is a pre-requisite for many autonomous, real-world systems because of the need for intelligent task assignment amongst a team for maximum efficiency. Similarly, agent failure, task, failure, and a lack of state information are inherent challenges when operating in complex environments. Many existing solutions make simplifying assumptions regarding the modeling of these factors, e.g., Markovian state information. However, it is not clear that this is always the appropriate approach or that results from these approaches are necessarily representative of performance in the natural world. In this work, we demonstrate that there exists a class of problems for which non-Markovian state modeling is beneficial. Furthermore, we present and characterize a novel heuristic for task allocation that incorporates realistic state and uncertainty modeling in order to improve performance. Our quantitative analysis, when tested in a simulated search and rescue (SAR) mission, shows a decrease in performance of more than 57% when a representative method with Markovian assumptions is tested in a non-Markovian setting. Our novel heuristic has shown an improvement in performance of 3-15%, in the same non-Markovian setting, by modeling probabilistic failure and making fewer assumptions.