Network Intrusion Detection With Auto-Encoder and One-Class Support Vector Machine

Abstract

Recent advances in machine learning have shown promising results for detecting network intrusion through supervised machine learning. However, such techniques are ineffective for new types of attacks. In the preferred unsupervised and semi-supervised cases, these newer techniques suffer from lower accuracy and higher rates of false alarms. This work proposes a machine learning model that combines auto-encoder with one-class support vectors machine. In this model, the auto-encoders learn the representation of the input data in a latent space and reduces the dimensionality of the input data. The dimensionality-reduced input is then extracted from the auto-encoder and passed to a one-class support vectors machine to classify the network event as an attack or a normal event. The model is trained on normal network events only. The proposed model is then evaluated and compared with several existing models. It achieves high accuracy when tested on the NSL-KDD and KDD99 datasets, with total accuracies of 96.24% and 99.45%, respectively.

Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism

“…Arivazhagi et al [10] presented an efficient unsupervised feature construction method based on the linear support vector machine model, and the research results proved its effectiveness. Abed Sa'ed et al [11] proposed a machine learning model combining a self-encoder with a class of support vector machines. The dimension of input data was reduced by the self-encoder, and the network events were classified by a class of support vector machines.…”