Journal:Computers & Security

Impact Factor:5.6

Abstract:With the popularity of Internet technology, industrial control systems (ICS) have started to access the Internet, which significantly facilitates engineers to manage ICS remotely but also exposes risks. Usually, an intrusion detection system (IDS) is used to secure network systems. Feature selection plays a crucial role in IDSs because detecting anomalies from high-dimensional network traffic features is time-consuming. However, few specific studies have been conducted for ICS. Many redundant features and data imbalance problems in ICS data lead to poor performance and low efficiency of generic IDS classification. In this paper, we design a genetic algorithm-based feature selection method for ICS characteristics. The proposed method incorporates a feature ranking fusion mechanism in the genetic algorithm for eliminating redundant features, enhances the global merit-seeking speed using the growing tree clustering idea, and we also design a new fitness function for ICS characteristics. The effectiveness and advancement of the proposed method are demonstrated on a real ICS dataset.

Key Words:Industrial control systems; Intrusion detection system; Feature selection; Genetic algorithmRank; AggreeGrowth tree

Indexed by:SCI JCR Q1

Note:https://www.sciencedirect.com/science/article/pii/S0167404823005850?dgcid=author

Discipline:Engineering

Document Type:JCR 一区

First-Level Discipline:Computer Science and Technology

Translation or Not:no