论文标题
使用低级矩阵的网络物理系统的基于无监督学习的强大多元入侵检测系统
Unsupervised Learning Based Robust Multivariate Intrusion Detection System for Cyber-Physical Systems using Low Rank Matrix
论文作者
论文摘要
关键基础设施（例如电力，运输，沟通等）的定期和不间断的操作对于一个国家的正常运作至关重要。过去导致关键基础设施服务中断的网络攻击被认为是一个重大威胁。随着技术的进步以及关键基础设施向基于IP的通信的进步，网络物理系统是攻击者的利润目标。在本文中，我们提出了一个称为RAD的强大多元入侵检测系统，用于检测O（d）空间和时间复杂性的网络物理系统中的攻击，其中D是系统状态向量中的数字参数。所提出的入侵检测系统（IDS）是在无监督的学习设置中开发的，而无需使用标记的数据表示攻击。它可以通过订阅强大的培训程序来损坏一小部分培训数据，或者在攻击中损坏。所提出的ID在几个现实世界数据集中的现有异常检测技术和攻击方案中都优于现有的异常检测技术。
Regular and uninterrupted operation of critical infrastructures such as power, transport, communication etc. are essential for proper functioning of a country. Cyber-attacks causing disruption in critical infrastructure service in the past, are considered as a significant threat. With the advancement in technology and the progress of the critical infrastructures towards IP based communication, cyber-physical systems are lucrative targets of the attackers. In this paper, we propose a robust multivariate intrusion detection system called RAD for detecting attacks in the cyber-physical systems in O(d) space and time complexity, where d is the number parameters in the system state vector. The proposed Intrusion Detection System(IDS) is developed in an unsupervised learning setting without using labelled data denoting attacks. It allows a fraction of the training data to be corrupted by outliers or under attack, by subscribing to robust training procedure. The proposed IDS outperforms existing anomaly detection techniques in several real-world datasets and attack scenarios.
