A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs

ASIAIR > College of Computer Science > Department of Computer Science and Information Engineering > Journal Artical > Item 310904400/115519

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/115519

Title:	A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs
Authors:	Gaura, Akshat;Gaurav, Akshat;Bhoosha, Brij;Gupta, Brij Bhooshan;Kumar, Prabin;Panigrahi, Prabin Kumar
Contributors:	資訊電機學院資訊工程學系
Keywords:	Flash crowd；Entropy；Machine learning；Small entrepreneurs
Date:	2022-NA
Issue Date:	2023-03-29 02:29:53 (UTC+0)
Publisher:	亞洲大學
Abstract:	The current COVID-19 issue has altered the way of doing business. Now that most customers prefer to do business online, many companies are shifting their business models, which attracts cyber attackers to launch several kinds of cyberattacks against commercial companies simultaneously. The most common and lethal DDoS attack disables the victim’s online resources. While large businesses can afford defensive measures against DDoS assaults, the situation is different for new entrepreneurs. Their lack of security resources restricts their ability to ward off DDoS attacks. Here, we aim to highlight the problems that prospective entrepreneurs should be aware of before joining the business, followed by a filtering mechanism that efficiently identifies DDoS assaults in the COVID-19 scenario, which is the subject of our research. The suggested approach employs statistical and machine learning techniques to discriminate between DDoS attack data and regular communication. Our suggested framework is cost-effective and identifies DDoS attack traffic with a 92.8% accuracy rate.
Appears in Collections:	[Department of Computer Science and Information Engineering] Journal Artical

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