A Deep Learning Approach for Anomaly Detection in Industrial Control Systems

B. Doraswamy, K. Krishna
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引用次数: 2

Abstract

An Industrial Control System (ICS) is essential in monitoring and controlling critical infrastructures such as safety and security. Internet of Things (IoT) in ICSs allows cyber-criminals to utilize systems' vulnerabilities towards deploying cyber-attacks. To distinguish risks and keep an eye on malicious activity in networking systems, An Intrusion Detection System (IDS) is essential. IDS shall be used by system admins to identify unwanted accesses by attackers in various industries. It is now a necessary component of each organization's security governance. The main objective of this intended work is to establish a deep learning-depended intrusion detection system that can quickly identify intrusions and other unwanted behaviors that have the potential to interfere with networking systems. The work in this paper uses One Hot encoder for preprocessing and the Auto encoder for feature extraction. On KDD99 CUP, a data - set for network intruding, we categorize the normal and abnormal data applying a Deep Convolutional Neural Network (DCNN), a deep learning-based methodology. The experimental findings demonstrate that, in comparison with SVM linear Kernel model, SVM RBF Kernel model, the suggested deep learning model operates better.
工业控制系统异常检测的深度学习方法
工业控制系统(ICS)在监测和控制安全和安保等关键基础设施方面至关重要。ics中的物联网(IoT)允许网络犯罪分子利用系统漏洞部署网络攻击。为了在网络系统中识别风险并监视恶意活动,入侵检测系统(IDS)是必不可少的。系统管理员应该使用IDS来识别来自不同行业的攻击者的非法访问。它现在是每个组织安全治理的必要组成部分。这项预期工作的主要目标是建立一个基于深度学习的入侵检测系统,该系统可以快速识别入侵和其他有可能干扰网络系统的不需要的行为。本文的工作采用一个Hot编码器进行预处理,一个Auto编码器进行特征提取。在网络入侵数据集KDD99 CUP上,采用基于深度学习的深度卷积神经网络(Deep Convolutional Neural network, DCNN)方法对正常和异常数据进行分类。实验结果表明,与SVM线性核模型、SVM RBF核模型相比,所提出的深度学习模型具有更好的运行性能。
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