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There are two approaches to address this problem either distinguish faulty data from real data, or detect which node is the compromised node and exclude it from the network. Although cryptographic mechanisms are used to protect sensor networks from masquerading attacks, attackers might compromise a node by stealing a key, and introducing afterwards faulty data from this compromised node. One of the main security problems in WSNs is the problem of compromised nodes, as defined in Zhang, Yu & Ning (2006). Classical detection techniques have been employed over lightweight Intrusion Detection Systems (IDSs), like the anomaly intrusion detection technique in Bhuse, Gupta & Al-Fuqaha (2007), to detect and deter attacks that affect the normal and uninterruptible operation of WSNs. As an important area of research, Intrusion detection has been applied in WSNs to enhance their security, despite the fact that it is used as a second line of defense. Intrusion Detection plays an active role in cybersecurity and many different technologies, tools, and approaches have been used in conjunction with it, as blockchain technology ( Agarwal et al., 2021), machine learning ( Dua & Du, 2016), and fuzzy logic ( Elhag et al., 2015). Several taxonomies of attacks on WSNs has been proposed in Han et al. Such characteristics have generated special attacking methods to WSNs, as the sleep deprivation attack ( Pirretti et al., 2005), the time synchronization attack ( Manzo, Roosta & Sastry, 2006), and the selective forwarding attack ( Yu & Xiao, 2006). Detection of masquerade attacks on WSNs requires lightweight techniques with respect to important WSN properties, like coverage, connectivity, data aggregation and specific communication patterns ( Bhuse & Gupta, 2006). Attacks on sensor networks routing (novel attacks-sinkhole and HELLO floods) have imposed new design for secure routing protocols ( Karlof & Wagner, 2003). As part of pervasive computing environments, WSNs raise fundamental security issues. The great range of applications has made WSNs very popular and the need for simple and familiar interactions more essential than ever. Wireless Sensor Networks (WSNs) were introduced some years ago as a new technology that combines wireless communication ( Alcaraz, 2019), computation, and sensing ( Stankovic, 2008). Experimental evaluations conducted on real network datasets, using IDSs of different capabilities, simulate special cases and compromised nodes in a WSN, verify the model efficiency, and show how the game should be played. The game is constructed and solved, by locating NE in pure and mixed strategies. The formal elements of the game are specified, the outcomes of the game are quantified by first specifying players’ preferences, and then, by using the von Neumann-Morgenstern utility function, and payoffs are obtained. The theoretical model represents these interactions in an extensive form game. The GIDS is responsible for making a final decision and for isolating a compromised node in case of an internal attack.
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The game is being played between a potential attacker, the nodes and the GIDS. Each LIDS gives suggestions whether the monitoring node is trusted or not. The model consists of several Local Intrusion Detection Systems (LIDSs), which are located to different nodes and communicate with a Global Intrusion Detection System (GIDS). It is an imperfect information game, formulated with the use of non-cooperative game theory, holding the assumption that all players are rational. A multiplayer game model is proposed as a solution to the problem of insider attacks in WSNs, the Game of Wireless Sensor Networks (GoWiSeN). Immediate defensive actions to isolate malicious nodes would mitigate any related impacts. In Wireless Sensor Networks (WSNs), significant security issues arise, including compromised nodes by insiders that disrupt the normal network operation. It is hard to detect them because of their privileges that partially protect them.
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Insiders might have incentives and objectives opposed to those of the belonging organization.