DETECTING DDOS ATTACK USING DEEP LEARNING TECHNIQUES

Author(s)	Puvaneswaran, Parthipan, Nivetha
Country	India
Abstract	In the modern digital world, the robustness of cybersecurity frameworks is crucial for maintaining operational integrity and safeguarding organizational assets. Distributed Denial of Service (DDoS) attacks are a frequent and very destructive kind of cyber intrusion that must be recognized and avoided, this study presents a Deep Learning. Based Intrusion Detection System (IDS). Distributed Denial of Service (DDOS)flooding is one of the security flaws that seriously damages IoT systems. DDOS assaults cannot be prevented by conventional data filtering methods. To safeguard the security of IoT settings, a novel hybrid deep CNN model-based framework for identifying DDoS flooding assaults is put forth in this study. It is utilized to satisfy the security needs of IoT settings and to overcome the drawbacks of existing DDoS attack detection approaches. One dimensional (1D) CNN and two dimensional (2D) CNN are used with two and three convolutional layers, respectively, to build a Hybrid Deep CNN model.
Keywords	Keywords – Distributed Denial of Service attack, Convolutional Neural Networking
Field	Computer > Network / Security
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-28
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.36380
Short DOI	https://doi.org/g86xb5

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International Journal For Multidisciplinary Research