One-Class Training for Intrusion Detection
Hamza Frihia, Halima Bahi · 2020
The extensive use of the internet has generated a large amount of data, and a new class of threats has arisen which led to the emergence of a new field of computer science: intrusion detection. An Intrusion Detection System (IDS) scans the data generated by the network traffic to detect potential attacks. In this paper, we propose a method for anomaly detection, in the network traffic, based on deep learning algorithms using a new approach called training objective. We propose to use a Deep Auto-Encoder (DAE) with One-Class training to analyze the contents of a dataset; as the normal accesses are more likely to be recognized, we utilize normal examples to train the DAE. Meanwhile, the dropout is applied to deal with the overfitting problem. We report experimental results regarding the accuracy, recall, and precision of the system on the well-known NSL-KDD benchmark dataset for network intrusion. Overall, the results indicate that the DAE model trained using One-class examples positively impacts the performance of the IDS.