An Adaptive and Lightweight Solution to Detect Mixed Rate IP Spoofed DDoS Attack in IoT Ecosystem

Abstract

Internet of Things (IoT) is a fast-growing and promising technology segment that aims to build advanced developments in automation and control, communication technologies, intelligent sensors etc. Despite various gains, it suffers from several security breaches or device malfunction that provoke catastrophic crashes in the IoT ecosystem. Distributed denial of service (DDoS) attack is a serious peril to the availability of IoT devises and services. DDoS is a diverse attack and the detection of mixed (High/Low) rate IP Spoofed DDoS attack in the IoT ecosystem is an arduous job because of its surreptitious nature and mixed rate traffic pattern. In this paper, we propose an adaptive and lightweight solution which detects and mitigates the mixed rate IP spoofed DDoS attack in the IoT ecosystem. The approach is implemented on the Contiki OS and the experimental results show that the solution is adaptive and is able to identify the attack with 99.1% accuracy.