Visible to the public Assessment of Machine Learning Techniques for Building an Efficient IDS

TitleAssessment of Machine Learning Techniques for Building an Efficient IDS
Publication TypeConference Paper
Year of Publication2020
AuthorsChytas, S. P., Maglaras, L., Derhab, A., Stamoulis, G.
Conference Name2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH)
Date Publishednov
KeywordsBuildings, CICIDS2017 dataset, complex environments, composability, computer network security, Cyber Attacks, cyberattack, DDoS Attacks, denial-of-service attack, efficient IDS, IDS, IDS dataset, Intrusion detection, Intrusion Detection Systems, learning (artificial intelligence), machine learning, machine learning techniques, pubcrawl, Real-time Systems, realistic background traffic, resilience, Resiliency, security, telecommunication security, telecommunication traffic
AbstractIntrusion 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.
Citation Keychytas_assessment_2020