RETRACTED ARTICLE: SybilWatch: a novel approach to detect Sybil attack in IoT based smart health care

Abstract

Internet of things (IoT) in health care is gaining popularity in the field of research to improve quality in smart health care systems and applications. However, security and privacy of Smart Health (S-Health) data are the challenging issues due to Sybil attacks. Sybil attack is one of the most common attacks where a malicious node uses morphed identities to generate Sybil nodes. Sybil nodes can acquire an authorized node identity and misbehaves by affecting its routing information, incurs interruption on communication line and storage. One of the IoT based smart health methodology is Privacy-Aware Smart Health (PASH), in which policy hiding is used to protect the privacy of users. The major issues in PASH is expensive to implement in S-Health applications, also it does not deal with attribute revocation and node traceability. To addresses these issues, a novel SybilWatch Enhanced Privacy-Aware Smart Health (E-PASH) approach is proposed in this paper. This approach has three major phases such as initialization phase, secure communication and Sybil node detection. A Lightweight Encryption Algorithm (LEA) is used to transmit SHRs (Smart Health Record) in encrypted form using prime order grouping. A novel BlueTits Detection (BTD) algorithm is used in detection phase where cluster head gathers the recent activities of the suspicious user, and based on the gathered parameters (Master key and Last One-Time Authentication), the cluster head declares it as a Sybil node. As soon as Sybil node is detected, revised revocation list is shared with active users. The proposed approach is less expensive compared to the existing approach, it also supports attribute revocation and node traceability which are the major setbacks is PASH. Simulation results and comparison analysis shows that proposed SybilWatch is efficient and cost effective compared to the existing approach, the proposed approach yields high detection rate of 99.7% and also false positive rate is reduced to 1% in the smart health systems, which is better compared to the existing approaches.