A feature selection based on genetic algorithm for intrusion detection of industrial control systems.
发布时间：2024-01-04 点击次数：
姚羽（教授）
+
- 博士生导师 硕士生导师
- 电子邮箱：
- 职务：复杂网络系统安全保障技术教育部工程研究中心主任
- 学历：博士研究生毕业
- 性别：男
- 联系方式：yaoyu@mail.neu.edu.cn
- 学位：博士
- 毕业院校：东北大学
- 所属院系：计算机科学与工程学院
- 学科：
计算机应用技术
计算机软件与理论
计算机系统结构
- 发表刊物：Computers & Security
- 影响因子：5.6
- 摘要：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.
- 关键字：Industrial control systems; Intrusion detection system; Feature selection; Genetic algorithmRank; AggreeGrowth tree
- 论文类型：SCI JCR Q2
- 备注：https://www.sciencedirect.com/science/article/pii/S0167404823005850?dgcid=author
- 学科门类：工学
- 文献类型：JCR 二区
- 一级学科：计算机科学与技术
- 是否译文：否