An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

Abstract

To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%.