Assessment of Machine Learning Techniques for Building an Efficient IDS
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Chytas, Sotirios Panagiotis | |
| dc.contributor.author | Maglaras, Leandros | |
| dc.contributor.author | Derhab, Abdelouahid | |
| dc.contributor.author | Stamoulis, George | |
| dc.date.accessioned | 2020-08-04T13:49:51Z | |
| dc.date.available | 2020-08-04T13:49:51Z | |
| dc.date.issued | 2020-11-05 | |
| dc.description.abstract | ntrusion 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. | en |
| dc.funder | No external funder | en |
| dc.identifier.citation | Chytas, S.P., Maglaras, L., Derhab, A. and Stamoulis, G. (2020) Assessment of Machine Learning Techniques for Building an Efficient IDS. First International Conference of Smart Systems and Emerging Technologies (SMARTTECH 2020), Prince Sultan University, Riyadh, Saudi Arabia , 3-5 November 2020 | en |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/20040 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | IEEE | en |
| dc.researchinstitute | Cyber Technology Institute (CTI) | en |
| dc.subject | IDS | en |
| dc.subject | Machine Learning | en |
| dc.title | Assessment of Machine Learning Techniques for Building an Efficient IDS | en |
| dc.type | Conference | en |