Development of an Adaptive Strategy for Detecting and Mitigating Rank Attacks Wireless Sensor Networks

Abstract

Rank attacks take advantage of the rank-based topology provided by the RPL (Routing Protocol for Low-Power and Lossy Networks) which interferes with the routing process of data transmission, leading to the decline in packet delivery and reliability of 6TiSCH networks. The aim of this study is to propose a framework called Adaptive Anomaly Detection with Objective Function Switching (ADAOFS) which facilitates the identification of rank attacks and provides an efficient solution to this problem while ensuring the safety, reliability, and energy efficiency of the network. An experimental approach with the use of Cooja/Contiki for modeling RPL- based 6TiSCH networks was utilized. In this experiment, the size of the network, deployment patterns utilized, and attack intensity were taken into consideration. The use of machine learning techniques allowed identifying anomalous routing activities. The adaptive objective function switching contributed to routing decisions. Performance was assessed with the help of PDR, energy consumption, and latency. The results demonstrated that both diminished and combined attacks may harm performance significantly, that is why ADAOFS remained effective in maintaining stable delivery of packets with a high level of energy efficiency (91.9) and short latency (17.2 ms).