Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks

Abstract

The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.

Identity Based Authentication Scheme (IAS) for Securing WSN Based Internet of Things

“…Hackers can infiltrate and hijack nodes, disrupting network communication or launching denialof-service attacks that cripple the entire network [13]. The physical accessibility of WSN nodes makes them vulnerable to physical attacks that can damage or destroy them, compromising data integrity and network functionality [14]. Implementing robust encryption protocols ensures data confidentiality and integrity during transmission and storage [15].…”