Impact of Machine Learning Algorithms in Intrusion Detection Systems for Internet of Things

Jinsi Jose, Deepa V. Jose, Karna Srinivasa Rao, Justin Janz
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引用次数: 1

Abstract

The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed.
机器学习算法在物联网入侵检测系统中的影响
由于物联网设备的大量使用,安全方面的重要性最近有所增加。使用物联网应用程序,保护系统免受各种漏洞的侵害是不可避免的。入侵检测系统是提供这种服务的动力机制。将人工智能引入入侵检测系统可以进一步增强其功能。本文试图了解机器学习算法在攻击检测中的影响。使用UNSW-NB 15数据集,评估了不同机器学习算法的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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