A Honeypot with Machine Learning based Detection Framework for defending IoT based Botnet DDoS Attacks

Ruchi Vishwakarma, A. Jain
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引用次数: 66

Abstract

With the tremendous growth of IoT botnet DDoS attacks in recent years, IoT security has now become one of the most concerned topics in the field of network security. A lot of security approaches have been proposed in the area, but they still lack in terms of dealing with newer emerging variants of IoT malware, known as Zero-Day Attacks. In this paper, we present a honeypot-based approach which uses machine learning techniques for malware detection. The IoT honeypot generated data is used as a dataset for the effective and dynamic training of a machine learning model. The approach can be taken as a productive outset towards combatting Zero-Day DDoS Attacks which now has emerged as an open challenge in defending IoT against DDoS Attacks.
基于机器学习的蜜罐检测框架,用于防御基于物联网的僵尸网络DDoS攻击
随着近年来物联网僵尸网络DDoS攻击的迅猛增长,物联网安全已成为网络安全领域最受关注的话题之一。该领域已经提出了许多安全方法,但在处理新出现的物联网恶意软件变体(称为零日攻击)方面仍然缺乏。在本文中,我们提出了一种基于蜜罐的方法,该方法使用机器学习技术进行恶意软件检测。物联网蜜罐生成的数据被用作机器学习模型的有效和动态训练的数据集。这种方法可以作为打击零日DDoS攻击的一个富有成效的开端,现在已经成为保护物联网免受DDoS攻击的公开挑战。
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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