• Khushwant Kaur
• Meena Jindal

The increasing prevalence and sophistication of Distributed Denial of Service (DDoS) attacks pose significant risks to the availability, integrity, and security of online services and infrastructures. Traditional detection methods, including shallow machine learning models, have proven inadequate in keeping pace with the evolving tactics of cyber attackers. In response, deep learning techniques have emerged as a powerful tool for detecting and classifying DDoS attacks with high accuracy and efficiency. This study explores the application of various deep learning models—such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid models combining autoencoders with multi-layer perceptron’s (MLPs)—to enhance DDoS attack detection and classification. The research addresses several key challenges, including data quality and volume, class imbalance, feature extraction, computational resource demands, adaptability to evolving threats, and integration with existing systems. Through a comprehensive review of recent studies, we identify the most effective deep learning architectures and methodologies for this purpose. Notably, some models have achieved over 99% accuracy on benchmark datasets, demonstrating the potential of deep learning in handling complex and imbalanced datasets effectively. The study also investigates the practical implementation of these models in resource-constrained environments, highlighting lightweight deep learning systems that achieve state-of-the-art detection accuracy with significantly reduced processing time. The findings underscore the potential of deep learning to revolutionize DDoS attack detection by providing advanced, efficient, and adaptive solutions. The deployment of sophisticated deep learning techniques is crucial for safeguarding digital infrastructures against the growing threat of DDoS attacks. This research contributes to developing more resilient and adaptive security solutions, laying the groundwork for robust defense mechanisms against future cyber threats. The continuous evolution of cyber threats necessitates ongoing research and development, ensuring that deep learning models remain effective and adaptive in the dynamic landscape of cybersecurity

cybersecurity, Attack, machine learning, deep learning, CNN

"LEVERAGING DEEP LEARNING FOR ENHANCED DETECTION AND CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.i297-i304, June-2024, Available :http://www.jetir.org/papers/JETIR2406830.pdf

"LEVERAGING DEEP LEARNING FOR ENHANCED DETECTION AND CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppi297-i304, June-2024, Available at : http://www.jetir.org/papers/JETIR2406830.pdf