Dimensionality reduction

Geochemistry

Pure mathematics

Support vector machine

Intrusion

Dimension (graph theory)

Operating system

The Internet

Data mining

Computer science

Geology

Mathematics

Artificial intelligence

Geometry

Intrusion detection system

Anomaly-based intrusion detection system

Machine learning

Reduction (mathematics)

International Journal of Information Technology

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.

A t-SNE based non linear dimension reduction for network intrusion detection