A Study on DDoS Attack Detection Technique Prior to Machine Learning in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are integral to a wide range of critical applications, including environmental monitoring, healthcare, and industrial control systems. Then delves into DDoS attacks, elucidating the various attack types, motives, and their detrimental effects on WSNs. The core of the study investigates DDoS detection techniques, encompassing signature-based, anomaly-based, and hybrid approaches. Optimization mechanisms for DDoS detection in WSNs are explored in-depth, including resource-efficient algorithms and scalability solutions. Consideration is given to striking a balance between detection accuracy and resource consumption, essential in the energy-limited world of WSNs. Machine learning and artificial intelligence's role in DDoS detection is elucidated, with a focus on feature selection, dimensionality reduction, and the application of various supervised and unsupervised learning algorithms. Cross-layer approaches, coordinating physical, data-link, and network layers, are discussed to enhance DDoS detection's robustness against sophisticated attacks. Energy efficiency considerations are integral, with strategies outlined to prolong sensor nodes' lifespans while ensuring reliable DDoS detection. The study culminates in the presentation of real-world experiments and simulations, offering insights into the practical performance of DDoS detection and optimization mechanisms. Various evaluation metrics are employed, such as detection rate, false positives, and resource utilization.