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%.
Highlights
With recent advancements in Internet-of-Things (IoT) technologies, it is expected that tens of billions of IoT devices will be interconnected by 2022 [1], and the usage of wireless sensor networks (WSNs) will grow quickly in various industry areas [2,3,4,5] as well as in military fields [6]
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
In this paper, we propose an enhanced trust mechanism based on a consensus-based false information filtering algorithm (TM-CFIFA) that can improve the trust evaluation process of existing trust mechanisms by using a false information filtering algorithm
Summary
With recent advancements in Internet-of-Things (IoT) technologies, it is expected that tens of billions of IoT devices will be interconnected by 2022 [1], and the usage of WSNs will grow quickly in various industry areas [2,3,4,5] as well as in military fields [6]. Energy-efficiency is another critical design factor to maximize the lifetime of WSNs [10,11]. To defend against insider attacks in WSNs, trust mechanisms (TMs) have been proposed and studied as a promising defense method [12,13,14]. Insider Attacks in WSNs. In WSNs, each sensor node sends its data packets toward the destination node by means of multi-hop collaboration. As shown, when source node A wants to send its packet to the destination node D, node A cannot directly send it to node D due to its limited energy or hardware capability [21]. Establishing mutual trust among inside nodes in WSNs are essential to guarantee that WSNs work correctly according to their design goals
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