A Fuzzy approach for DDoS attack detection in Digital Forensics

RESHMA A S

Fuzzy Approach,DDo Attack,Digital Forensics,Network Congestion,Inter-node communication

Denial of Service (DoS) attacks are presumed to be severe threats on the web, which aim to hinder the network’s bandwidth, thus disrupting the network operations (Cheema et al., 2022). The DoS attacks involve the operation interruption of a system/network/service. It happens through a single-source attack (Syed et al., 2020). Distributed denial of service attack (DDoS), a sub-category of DoS, is confronted by multiple sources to compromise the network (Agrawal & Vieira, 2013; Mittal et al., 2023). In DDoS attacks, cybercriminals often target firewalls, routers, and links, causing damage to the victim’s defense system either for ransom or personal target (Bawany et al., 2017). This attack often results in enormous losses for the sector involved in online services. In this context, digital forensics, a category of online service, is a practical approach for cyber specialists in gathering digital evidence to uncover fraudulent activities in online media; it is also prone to cyber-attacks. Malicious intruders in this situation often target digital forensic experts’ tools; however, a DoS attack is widespread and tampers with the forensic investigation process. This issue highlights the need for an immediate approach to confront the DoS/DDoS issue posed by cybercriminals (Pandey et al., 2020). DDoS attacks have been in research studies for many decades; nevertheless, intense aftereffects were understood after witnessing the disruption of popular websites Amazon, Spotify, and Twitter in 2016 by DDoS attacks (Twitter, Amazon, Spotify Went down Following Massive DDoS Attack, 2016). This paper proposes to employ fuzzy logic, which can act as a countermeasure with its power to identify malicious packets and, therefore, take proper actions in preventing DDoS attacks (Iyengar et al., 2014). A DoS/DDoS attack is considered the most dangerous among other cyber threats, for which disruption technology like machine learning has been employed in identifying DDoS attacks to guarantee multilayer privacy concerns (Kumari & Mrunalini, 2022).

Volume 10 Issue 1, January-2025

Pages : a931-a935