DDoS analysis using machine learning: survey, issues, and future directions

Lalmohan Pattnaik, Suneeta Satpathy, Bijay Kumar Paikaray and Pratik Kumar Swain

International Journal of Business Continuity and Risk Management, 2024, vol. 14, issue 1, 57-76

Abstract: Technology has evolved as humanity's new religion in this generation. With everyone switching to online services for their work during the COVID-19 pandemic, digitisation increased more sharply afterwards. The distributed denial of service (DDoS) assault is one of many online dangers that needs to be taken seriously by companies or customers offering cloud services or in need of services respectively. Such threats make the customers deprived of cloud services by overburdening the network with the number of packets causing the shutdown of cloud services. In order to trick current detection systems, attackers are also evolving with the technologies and modifying their attack strategies. Every day, enormous amounts of data are produced, processed, and stored, with typical detection technologies unable to identify new and sophisticated DDoS attacks. This research study thoroughly examines the previous work on DDoS threat analysis using machine learning, as well as its difficulties and potential future applications.

Keywords: denial of service; DoS; distributed denial of service; DDoS; machine learning; cloud service. (search for similar items in EconPapers)
Date: 2024
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