dc.contributor.advisor |
Azar, Jimmy |
dc.contributor.author |
Kassab, Ali |
dc.date.accessioned |
2021-09-09T03:20:10Z |
dc.date.available |
2021-09-09T03:20:10Z |
dc.date.issued |
9/9/2021 |
dc.date.submitted |
9/9/2021 |
dc.identifier.uri |
http://hdl.handle.net/10938/22995 |
dc.description.abstract |
One-class classification has been a promising direction in capturing the properties of a target class. Under multiclass classification problems with severe imbalance in target labels, research proposes the decomposition of a given problem into multiple sub-problems trained as separate one-class classifiers. We propose a sequential multi-stage one-class classification model to detect anomalies found in a multiclass classification context - a network intrusion detection system. We experiment with the model and test its performance using the NSL-KDD dataset, a modified version of the KDD’99 dataset. The model consists of several stages; we start with an initial classifier to detect the presence of an anomaly, followed by a sequence of per class one-class classifiers that will classify the intrusion based on the current class or otherwise pass to the next classifier trained on a less common attack type. Finally, we provide the analysis of our contribution compared to multiclass models trained over the dataset observations, and treated with an imbalanced learning approach. |
dc.language.iso |
en |
dc.subject |
One-Class Classification |
dc.subject |
Anomaly Detection |
dc.subject |
Machine Learning |
dc.subject |
Network Intrusion Detection |
dc.title |
A Sequential Multi-Stage One-Class Classification Model in Network Intrusion Detection Systems |
dc.type |
Thesis |
dc.contributor.department |
Department of Industrial Engineering and Management |
dc.contributor.faculty |
Maroun Semaan Faculty of Engineering and Architecture |
dc.contributor.institution |
American University of Beirut |
dc.contributor.commembers |
Al-Qaisi, Saif |
dc.contributor.commembers |
Moacdieh, Nadine |
dc.contributor.degree |
ME |
dc.contributor.AUBidnumber |
202023490 |