| Title | Multi-Perspective Machine Learning a Classifier Ensemble Method for Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Miller, Sean T., Busby-Earle, Curtis |
| Conference Name | Proceedings of the 2017 International Conference on Machine Learning and Soft Computing |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4828-7 |
| Keywords | cybersecurity, E-Government, ensemble methods, Human Behavior, Intrusion detection, machine learning, policy-based governance, pubcrawl, resilience, Resiliency |
| Abstract | Today cyber security is one of the most active fields of re- search due to its wide range of impact in business, govern- ment and everyday life. In recent years machine learning methods and algorithms have been quite successful in a num- ber of security areas. In this paper, we explore an approach to classify intrusion called multi-perspective machine learn- ing (MPML). For any given cyber-attack there are multiple methods of detection. Every method of detection is built on one or more network characteristic. These characteristics are then represented by a number of network features. The main idea behind MPML is that, by grouping features that support the same characteristics into feature subsets called perspectives, this will encourage diversity among perspectives (classifiers in the ensemble) and improve the accuracy of prediction. Initial results on the NSL- KDD dataset show at least a 4% improvement over other ensemble methods such as bagging boosting rotation forest and random for- est. |
| URL | http://doi.acm.org/10.1145/3036290.3036303 |
| DOI | 10.1145/3036290.3036303 |
| Citation Key | miller_multi-perspective_2017 |
Multi-Perspective Machine Learning a Classifier Ensemble Method for Intrusion Detection