Multi-head Attention based Deep Learning Framework for Identifying Distributed Denial of Service Attacks

The increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks pose severe threats to network security, necessitating more advanced detection mechanisms. Traditional detection methods, which rely on statistical analysis and classical machine learning, often struggle to generalize due to their reliance on handcrafted features. To address these limitations, this paper proposes a Multi-head Attention Distributed Denial of Service Network (MADDoSNet), a deep learning-based framework that leverages a multi-head attention mechanism to effectively analyze sequential network traffic and identify attack patterns. Multi-head attention enables the model to focus on multiple aspects of network behavior simultaneously, improving feature extraction and anomaly detection in complex, high-dimensional data. Following an extensive evaluation on the ISCX2012 dataset, the proposed MADDoSNet demonstrates superior detection accuracy compared to state-of-the art models. Key performance metrics such as precision, recall, F1-score, and AUC confirm its effectiveness in distinguishing normal and malicious traffic. Moreover, we assess the computational efficiency of MADDoSNet, highlighting its trade-offs in terms of inference speed and resource consumption. Our findings underscore the potential of attention-based deep learning frameworks in cybersecurity, particularly in mitigating evolving network threats.