V3I3P5

Detecting Distributed Denial of Service Attacks: Recent Developments and Future Techniques

Mrs. G. Sathya^1*, Dr. D. Rajakumari²

Abstract

Distributed Denial of Service (DDoS) attacks, which cause online services to breakdown by flooding them with excessive traffic, remain a major threat to network security. The necessity for sophisticated detection methods that can effectively identify and neutralize these threats has been brought to light by the growth of these assaults as well as the rise in connected devices. An overview of the conventional DDoS detection techniques, including threshold-based and approaches based on signatures, which have been widely employed because of their ease of use. Nevertheless, these techniques frequently fail to control new assault patterns, necessitating the use of more advanced strategies.The article then looks at contemporary methods that improve DDoS detection by combining artificial intelligence, deep learning, and machine learning. Because of their increased detection accuracy and capacity to adjust to novel assault patterns, these data-driven techniques have become increasing popularity. These methods make it possible to classify traffic in real-time and reduce false positives by using past traffic data, which increases their effectiveness in dynamic attacks. Surroundings. Additionally, the study discusses issues including scalability, real-time detection, and the intricacy of changing attack routes that make conventional detection techniques more difficult to use.Lastly, the research looks at hybrid detection models, which integrate many methods to provide a more complete defence against DDoS assaults. These models increase overall detection accuracy and system resilience by combining the best features of traditional and contemporary methodologies. The final section of the study discusses the potential applications of DDoS detection, highlighting the necessity of intelligent, scalable systems that can adjust to the rising assault complexity in a society that is becoming more interconnected.

Keywords:

Distributed Denial of Service (DDoS), Network Security, Traffic Classification, Hybrid Detection Models