FEATURE SELECTION BASED LIGHTWEIGHT IDS FOR RESOURCE CONSTRAINED IOT: A COMPREHENSIVE SURVEY

Abstract

 

Abstract

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are of significant importance in the field of cybersecurity, as the threat posed by these attacks continues to increase each year. These attacks particularly exploit Internet of Things (IoT) environment, as IoT networks are often consist of devices with limited computational resources. The rapid proliferation of IoT devices, combined with their inherent resource constraints, makes them an easy target for launching DoS/DDoS attacks. Understanding the severity and exploitation of these attacks, this study presents a comprehensive survey and analysis focusing on DoS/DDoS attacks, including an in-depth discussion of their types within the IoT context. The study highlights the inherent characteristics of IoT systems, particularly resource limitations such as limited processing power, memory, and energy in IoT devices. Furthermore, this work explores Intrusion Detection Systems (IDS) and recent advancements in attack detection followed by Machine Learning (ML) techniques used for detecting DoS/DDoS attacks. This survey also examines state-of-the-art ML-based lightweight IDS for DoS/DDoS detection. Finally, this paper discusses future research directions required for designing effective DoS/DDoS attack mitigation solutions for resource constrained IoT systems.

Keywords

DoS/DDoS attack, Internet of Things, Network Security, Machine Leaning

Article History

Received on 24 March, 2026

Accepted on 20 April, 2026

Published on 21 April, 2026

Copyright @Author

Corresponding Author:

Dr. Mahawish Fatima