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

ASIA unversity > 資訊學院 > 資訊工程學系 > 期刊論文 > Item 310904400/115519

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题名:	A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs
作者:	Gaura, Akshat;Gaurav, Akshat;Bhoosha, Brij;Gupta, Brij Bhooshan;Kumar, Prabin;Panigrahi, Prabin Kumar
贡献者:	資訊電機學院資訊工程學系
关键词:	Flash crowd；Entropy；Machine learning；Small entrepreneurs
日期:	2022-NA
上传时间:	2023-03-29 02:29:53 (UTC+0)
出版者:	亞洲大學
摘要:	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.
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