| Title | A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Afroz, Sabrina, Ariful Islam, S.M, Nawer Rafa, Samin, Islam, Maheen |
| Conference Name | 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) |
| Date Published | dec |
| Keywords | Anomaly, Computational modeling, feature selection, Industries, Intrusion detection, Measurement, Organizations, privacy, pubcrawl, Random Forest, random forests, Servers, Support vector machines, SVM, threat vectors |
| Abstract | Unauthorized access or intrusion is a massive threatening issue in the modern era. This study focuses on designing a model for an ideal intrusion detection system capable of defending a network by alerting the admins upon detecting any sorts of malicious activities. The study proposes a two layered anomaly-based detection model that uses filter co-relation method for dimensionality reduction along with Random forest and Support Vector Machine as its classifiers. It achieved a very good detection rate against all sorts of attacks including a low rate of false alarms as well. The contribution of this study is that it could be of a major help to the computer scientists designing good intrusion detection systems to keep an industry or organization safe from the cyber threats as it has achieved the desired qualities of a functional IDS model. |
| DOI | 10.1109/WIECON-ECE52138.2020.9397945 |
| Citation Key | afroz_two_2020 |
A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine