Dalmaz, HasanErdal, ErdalÜnver, Halil Murat2025-01-212025-01-212021978-166542908-5https://doi.org/10.1109/UBMK52708.2021.9558930https://hdl.handle.net/20.500.12587/231486th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 176826Developing technology brings many risk in terms of data security. In this regard, it is an important issue to detect attacks for network security. Intrusion detection systems developed due to technological developlments and increasing attack diversity have revealed the necessity of being more succesful in detecting attacks. Today, many studies are carried out on this subject. When the literature is examined, there are various studies with varying success rates in detecting network attacks using machine learning approaches. In this study, the NSL-KDD dataset was explained in detail, the positive aspects of the KDD Cup 99 dataset were specified, the classifier used, performance criteria and the success results obtained were evaluated. In addition, the developed GWO-MFO hybrid algorithm is mentioned and the result is shared. © 2021 IEEEeninfo:eu-repo/semantics/closedAccessClassifier; Machine learning; Network attacks; NSL-KDD; Performance evaluationConference Object52252710.1109/UBMK52708.2021.95589302-s2.0-85125879577N/A