Deep Learning-Driven Intrusion Detection System for Distributed Denial of Service Mitigation

Abstract

DDoS attacks continue to pose a serious risk to digital infrastructures, as they can render online services inaccessible without altering system files or gaining direct control over the target. Traditional security mechanisms often fall short in identifying these attacks promptly due to their massive scale and the subtlety with which they blend into regular traffic. With the advancement of artificial intelligence, especially in the realm of deep learning, new solutions are emerging to enhance the detection and classification of such threats. In this work, we focus on strengthening Intrusion Detection Systems (IDS) by leveraging deep learning methods to improve accuracy and responsiveness in detecting DDoS attacks. Using the comprehensive CIC-DDoS-2019 dataset, we experimented with several deep learning architectures including Feedforward Neural Networks (MLP), Convolutional Neural Networks (CNN), and Recurrent models incorporating Long Short-Term Memory (LSTM). These models were evaluated for their ability to analyze complex traffic behaviors and identify malicious activity within diverse network environments. his study contributes to the ongoing research on intelligent cybersecurity solutions by proposing deep learning-based IDS frameworks that not only detect threats with higher accuracy but also adapt to dynamic attack patterns. Our findings suggest that such models can serve as a critical component in modern security infrastructures, offering scalable and resilient defense mechanisms against increasingly sophisticated cyberattacks like DDoS. Our empirical results demonstrate that the MLP model yielded the most reliable performance, achieving an outstanding classification precision of 99.62% across various traffic categories. This highlights its effectiveness in isolating harmful flows from legitimate ones, thereby reducing the risk of false alarms and improving detection reliability.