Intrusion Detection System for Industrial Control Systems Based on Imbalanced Data

Abstract

The integration of industrialization and informatization has exposed industrial control systems (ICSs) to increasingly serious security challenges. Currently, the mainstream method to protect the security of ICSs is intrusion detection system (IDS) based on deep-learning. However, these methods depend on a massive amount of high-quality data. Owing to the characteristics and protocol limitations, ICSs data usually experience low-quality and data imbalance problems, which significantly affects the accuracy of IDS.In this study, an IDS for ICS that combines data expansion algorithm and CNN was proposed. A novel normalized neighborhood weighted convex combined random sample (NNW-CCRS) oversampling algorithm was designed, which automatically attenuates the effects of noise and expanding imbalanced data to produce balanced ICS datasets. By reducing the impact of imbalanced ICS data on IDSs, our system effectively protects the security of ICS. Secure Water Treatment dataset (SWaT) was used for experimental validation. The experimental results confirmed that the accuracy of the proposed system improved by approximately 20%, compared to the ICS without data expansion.