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

doi:10.1016/j.techfore.2022.121554

Technological Forecasting and Social Change

Volume 177, April 2022, 121554

https://doi.org/10.1016/j.techfore.2022.121554 Get rights and content

Highlights

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DDoS attack detection in COVID-19 Scenario for Small Entrepreneurs is not well-defined and hardly investigated.
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Sometimes Flash Crowd falsely filtered out by DDoS detection algorithms.
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The proposed approach uses statistical and Machine learning techniques for the detection of DDoS attack and Flash crowd.
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The proposed approach is efficient and simple to implement by Small Entrepreneurs.

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.

Keywords

DDoS

Flash crowd

Entropy

Machine learning

Small entrepreneurs

Cited by (0)

Akshat Gaurav received the M.Tech degree in Computer Engineering (cyber security) from NIT Kurukshetra, India. His research interests include information security, cyber security, cloud computing, web security, intrusion detection, and computer network.

B. B Gupta earned a Ph.D. degree in information and cybersecurity from IIT Roorkee, India. He has published more than 400 research articles (including four books and 20 book chapters) in international journals and high-repute conferences, including the IEEE, Elsevier, ACM, Springer, and Inderscience. His research interests include cybersecurity, information security, smartphone, web security, cloud computing, computer networks, intrusion detection, and phishing.

Prabin Kumar Panigrahi is a professor of Information Systems department at the Indian Institute of Management Indore, India. He earned his Ph.D from Indian Institute of Technology, Kharagpur. His research interests include Emerging Technologies, Machine Learning, Cyber Security, Cyber Laws, Information Security, Text Mining of Vernacular Languages, Technology Adoption, e-Governance, e-Learning, e-Participation, Social Inclusion in Information Systems, and Business Value of Information Systems.

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