A t-SNE based non linear dimension reduction for network intrusion detection
| Autor: | M. Sugumaran, Yasir Hamid |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science Applied Mathematics Dimensionality reduction 020206 networking & telecommunications 02 engineering and technology Intrusion detection system computer.software_genre Computer Science Applications Support vector machine Nonlinear system Data visualization Computational Theory and Mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing The Internet Network intrusion detection Data mining Electrical and Electronic Engineering Day to day business computer Information Systems |
| Zdroj: | International Journal of Information Technology. 12:125-134 |
| ISSN: | 2511-2112 2511-2104 |
| Popis: | With the increased dependence on the internet for day to day activities, the need to keep the networks secure has become more vital. The quest of securing the computer systems and networks, from the users with destructive mindset, has resulted in the invention of surfeit devices and methods. One such method against whom the responsibility of discriminating between normal and harmful data, flowing on the network is, intrusion detection system (IDS). In this work an IDS model based on support vector machines is proposed. In order to enhance the detection capability of support vector machine based model for intrusion detection, and to eliminate the inherent problem of intrusion detection i.e, low accuracy of the system in detecting user to root and remote to local attacks, this paper proposes to use recent non-linear dimension reduction technique to enhance the discrimination of the data. Results demonstrate that t-SNE based dimension reduction improve the accuracy of SVM for network intrusion detection system. A comparison of the proposed system with the previous works has proven that this work has enhanced detection rate for almost all the attack groups. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink