An efficient attack detection framework for secure smart health care system

Abstract

Healthcare internet of things (iot) is gaining prominence in the area of research to increase the efficiency of intelligent healthcare networks and applications. however, smart health(s-health) data protection and safety face different threats, such as sybil, mitm, clone, ddos, etc. this research on s-health suggests a stable framework called multi attack detection framework (madf) that detects three different attacks: sybil attack, mitm, and clone attack. sybil attack is one of the most frequent assaults, in which malicious nodes generate sybil nodes using morphic identities. sybil nodes can gain allowed identities and disconformities by routing information and communication line interruptions and storage. privacy-aware digital health (pash), a smart health approach built on iot, is used to secure users confidentiality through regulation hiding. in s-health applications, pash is very costly to introduce. it is also not about the revocation of attributes and traceability of nodes. in the context of this study, we suggest a novel solution to sybilwatch enhanced privacy-aware smart health (e-pash). this strategy consists of three main phases: initialization, safe contact, and identification of the sybil nodes. using a prime order grouping, smart health record (shr) transmits a lightweight encryption algorithm (lea) in encrypted form. in the detection process, the cluster head collects recent unusual user behaviours. the cluster head announces that it is a sybil node based on collected parameters (master key and last one-time authentication). the revamped revocation list of active users is exchanged until the sybil node is identified. simulation findings and comparative analyses have shown that the sybilwatch proposed is reliable and cost-efficient related to the current solution. the proposed approach has an identification rate of 97% and even a false positive 1% of reduction in smart health networks, which is higher than the conventional approaches. newline newline newline