Improving Accuracy for Intrusion Detection Through Layered Approach Using Support Vector Machine with Feature Reduction
| Title | Improving Accuracy for Intrusion Detection Through Layered Approach Using Support Vector Machine with Feature Reduction |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Nema, Aditi, Tiwari, Basant, Tiwari, Vivek |
| Conference Name | Proceedings of the ACM Symposium on Women in Research 2016 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4278-0 |
| Keywords | genetic algorithm, Human Behavior, Metrics, NSL KDD dataset, pubcrawl, support vector machine, Support vector machines, threat vectors |
| Abstract | Digital information security is the field of information technology which deal with all about identification and protection of information. Whereas, identification of the threat of any Intrusion Detection System (IDS) in the most challenging phase. Threat detection become most promising because rest of the IDS system phase depends on the solely on "what is identified". In this view, a multilayered framework has been discussed which handles the underlying features for the identification of various attack (DoS, R2L, U2R, Probe). The experiments validates the use SVM with genetic approach is efficient. |
| URL | http://doi.acm.org/10.1145/2909067.2909100 |
| DOI | 10.1145/2909067.2909100 |
| Citation Key | nema_improving_2016 |