观察看不见的:人工智能驱动的方法来检测对关键基础设施的攻击

Domenico Lofú, Andrea Pazienza, Agostino Abbatecola, E. Lella, Nicola Macchiarulo, Pietro Noviello
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引用次数: 0

摘要

随着运营技术(OT)网络与信息技术(IT)通信的日益融合,关键基础设施面临着可能造成巨大危害的新威胁。研究保护机制和开发能够防止此类攻击的安全系统是当今最重要的。除了正式定义代表化工行业IT和OT网络交织的模型外,我们还通过实施基于深度学习(DL)的入侵检测系统(IDS),证明了检测不同类型攻击的能力,并取得了良好的实验结果,其准确率达到87,19%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Watching against the Unseen: AI-powered Approach to Detect Attacks on Critical Infrastructure
The increasing convergence of Operational Technology (OT) networks into Information Technology (IT) communications poses critical infrastructures to new threats that may cause huge hazards. The study of protection mechanisms and the development of security systems capable of preventing such attacks is of paramount importance nowadays. Besides formally defining the model representing the intertwining of IT and OT networks of a Chemical Industry, we prove the ability to detect different types of attacks with good results experimentally by implementing an Intrusion Detection System (IDS) based on Deep Learning (DL) that achieves an accuracy of 87, 19%.
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