| dc.creator | Chytas S.P., Maglaras L., Derhab A., Stamoulis G. | en |
| dc.date.accessioned | 2023-01-31T07:47:24Z | |
| dc.date.available | 2023-01-31T07:47:24Z | |
| dc.date.issued | 2020 | |
| dc.identifier | 10.1109/SMART-TECH49988.2020.00048 | |
| dc.identifier.isbn | 9781728174075 | |
| dc.identifier.uri | http://hdl.handle.net/11615/72915 | |
| dc.description.abstract | 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. © 2020 IEEE. | en |
| dc.language.iso | en | en |
| dc.source | Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020 | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099295427&doi=10.1109%2fSMART-TECH49988.2020.00048&partnerID=40&md5=ce674665b2a4100735d7911aaf2fbe13 | |
| dc.subject | Denial-of-service attack | en |
| dc.subject | Intrusion detection | en |
| dc.subject | Machine learning | en |
| dc.subject | Background traffic | en |
| dc.subject | Complex environments | en |
| dc.subject | Cyber-attacks | en |
| dc.subject | DDoS Attack | en |
| dc.subject | Intrusion Detection Systems | en |
| dc.subject | Machine learning techniques | en |
| dc.subject | Potential threats | en |
| dc.subject | Real time | en |
| dc.subject | Network security | en |
| dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
| dc.title | Assessment of Machine Learning Techniques for Building an Efficient IDS | en |
| dc.type | conferenceItem | en |
