Contribution of wavelets to cybersecurity: Intrusion detection systems using neural networks

Abstract

The gigantic growth of the exchanged digital data has raised important security challenges. In this ecosystem, connected objects, systems and networks are exposed to various cyber threats endangering sensitive data and compromising confidentiality, integrity and authentication. Modelling intrusion detection systems (IDS) constitute an important research field with a major goal to protect targeted systems and networks against malicious activities. Many network IDS have been recently designed with artificial intelligence techniques. Signal processing techniques have been applied in network detection systems due to their ability to help for a good intrusion detection. At the same context, the wavelet transform which is considered as a very efficient tool for the decomposition and reconstruction of signals can be recommended in the design of powerful network detection systems, and can be applied for data preprocessing denoising and extracting information. Wavelets combined to neural networks can be useful for modelling intrusion detection with the main challenges to reduce the false alarms, increase the test accuracy and increase novel attacks detection rate. In this work, we present a major contribution in the research field to better understand how wavelets and neural networks can be combined for modelling efficient IDS.