Chiriac Beatrice-Nicoleta, Anton Florin-Daniel, Ioniță Anca-Daniela, Vasilică Bogdan-Valentin
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computer Sciences, National University of Science and Technology Polithenica Bucharest, 313 Spl. Independenței, RO060042 Bucharest, Romania.
Sensors (Basel). 2024 Dec 28;25(1):130. doi: 10.3390/s25010130.
Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.
每天都有大量新的网络安全攻击被报道，传统的防御方法难以应对。在工业环境处理大量数据的数字时代背景下，需要新的网络安全解决方案，基于人工智能（AI）算法的入侵检测系统（IDS）正在为这一关键问题提供答案。本文提出了一种通过整合英伟达Morpheus开源AI框架的计算优势来实现工业4.0基于网络的入侵检测系统通用模型的方法。该解决方案通过两个用于数据分析的管道进行模块化构建。这些管道使用了一个预训练的XGBoost（极端梯度提升）模型，其准确率高达90%。由于应用了联邦学习方法，所提出的IDS具有快速的分析速度，几乎能在10秒内处理超过500,000个输入。通过集成生成多态网络流量数据包的生成对抗网络（GAN），提高了模型的分类性能。
