Machine Learning Algorithms for Predicting and Mitigating DDoS Attacks

Abstract

Distributed Denial of Service (DDoS) attacks pose a severe threat to network infrastructures, causing downtime and significant financial losses. Machine learning (ML) algorithms have emerged as a promising approach for predicting and mitigating these attacks. This abstract explores the application of ML in tackling DDoS attacks, focusing on predictive modeling and mitigation strategies. Predictive modeling involves using historical attack data to train supervised learning algorithms such as Support Vector Machines (SVM), Random Forests, and Neural Networks. These models analyze network traffic patterns to detect anomalies indicative of potential DDoS attacks. Feature selection techniques enhance model accuracy by identifying critical indicators of attack behavior. Mitigation strategies leverage ML algorithms in real-time to distinguish between legitimate and malicious traffic during an attack. Anomaly detection algorithms like k-means clustering and Isolation Forests flag abnormal traffic patterns, triggering adaptive responses such as traffic rerouting or filtering through Intrusion Prevention Systems (IPS). Challenges include the dynamic nature of network traffic and the need for robust, scalable algorithms capable of processing vast datasets in real-time. In conclusion, ML algorithms offer effective tools for predicting and mitigating DDoS attacks by enhancing detection accuracy and response capabilities. Future advancements will focus on improving algorithm efficiency and resilience against evolving attack strategies.