Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers

ASIAIR > College of Computer Science > Department of Computer Science and Information Engineering > Journal Artical > Item 310904400/115592

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/115592

Title:	Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers
Authors:	Bhoosha, Brij;Gupta, Brij Bhooshan;Chaudh, Pooja;Chaudhary, Pooja;Chan, Xiaojun;Chang, Xiaojun;Nedjah, Nadia;Nedjah, Nadia
Contributors:	資訊電機學院資訊工程學系
Keywords:	Internet of things (IoT) networksDistributed Denial of Service (DDoS) attackConsumer IoT (CIoT) devicesMachine learning algorithmsBotnetIoT security
Date:	2022-02-01
Issue Date:	2023-03-29 02:50:22 (UTC+0)
Publisher:	亞洲大學
Abstract:	From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks.
Appears in Collections:	[Department of Computer Science and Information Engineering] Journal Artical

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	104	View/Open

Loading...