Modeling and Detecting of Sybil Attacks in RPL for IoT Based on Fuzzy Artificial Bee Colony

B IPv6-based low-power and lossy networks (LLNs) are one of the major enablers for the Internet of Things (IoT). IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is proposed and standardized as a routing protocol for LLNs. RPL is vulnerable to various security attacks because of the lack of specific security models and its resource-constrained nature. One of the most destructive attacks is the Sybil attack. In a Sybil attack, a malicious thing claims multiple illegitimate identities. The target of this attack is to deplete the limited energy resource of legitimate things. Modeling the Sybil attack in the IoT environment as well as detecting and mitigating malicious things. In step one, we propose a fuzzy artificial bee colony (FABC) based on Sybil attack modeling. In step two, we propose a lightweight countermeasure to effectively detect and mitigate the Sybil malicious thing. The proposed method is simulated in Contiki Cooja. The average accuracy, sensitivity, and specificity of the proposed method are more than 94%, 93%, and 85%, respectively. In addition to accuracy, sensitivity, specificity, and F-score, the proposed method was also evaluated with packet delivery ratio and energy consumption criteria and presents desirable results. The results show that the proposed approach can improve security in the RPL protocol for the IoT based on the detection of Sybil attacks. The results show that the proposed method, in addition to having high accuracy in detecting attacks, prevents the wastage of energy resources by efficient detection of attacks.