Trust management in wireless sensor networks

Abstract

SUMMARYThe range of applications of wireless sensor networks is so wide that it tends to invade our every day life. In the future, a sensor network will survey our health, our home, the roads we follow, the office or the industry we work in or even the aircrafts we use, in an attempt to enhance our safety. However, the wireless sensor networks themselves are prone to security attacks. The list of security attacks, although already very long, continues to augment impeding the expansion of these networks. The trust management schemes consist of a powerful tool for the detection of unexpected node behaviours (either faulty or malicious). Once misbehaving nodes are detected, their neighbours can use this information to avoid cooperating with them, either for data forwarding, data aggregation or any other cooperative function. A variety of trust models which follow different directions regarding the distribution of measurement functionality, the monitored behaviours and the way measurements are used to calculate/define the node's trustworthiness has been presented in the literature. In this paper, we survey trust models in an attempt to explore the interplay among the implementation requirements, the resource consumption and the achieved security. Our goal is to draw guidelines for the design of deployable trust model designs with respect to the available node and network capabilities and application peculiarities.

Detecting Malicious Data Injections in Wireless Sensor Networks

“…Yu et al [2012] lists all the threats that can be mitigated by trust management, including "Stealthy attacks" -a kind of malicious data injection -but these are not analysed in detail. Similarly, Zahariadis et al [2010a] build a taxonomy of trust metrics, which includes consistency of reported values/data, but they focus mostly on the other network-related metrics. Also Shen et al [2011] survey defensive strategies against attacks to the network layer.…”

“…Trust-management techniques with exchanges of trust information (e.g., [Bao et al 2012;Ganeriwal et al 2003;Momani et al 2008]) are also suited for mobility, since a sensor i which migrates to a new area and becomes a neighbour of j, can benefit of recommendations from sensors which have been j's neighbours in the past [Zahariadis et al 2010b]. So far, exchanges of trust informations have been considered without investigating the effects of mobility, therefore sensor i will generally maintain indirect information about sensor j only if there is interaction between i and j, and i cannot observe j's behaviour (e.g., it is not in the wireless communication range).…”

A framework for service provisioning in virtual sensor networks

“…The attacks that a trust management system can detect depend on the number and type of node behavior aspects that are monitored [39] and range from network up to application layer attacks (data inconsistency). Trading off hardware resource requirements for the realization of the trust system and mitigation of the most probable attacks, the VSN trust system will be capable of detecting the black-hole, selective forwarding, denial of routing service, and selfish behavior attack.…”

Design And Implementation Of a Trust-Aware Routing Protocol For Large WSNs

“…As the research work on routing attacks in WSNs has continued, trust models fitting different network architectures and different levels of security requirements have been proposed. A more elaborate review of the trust models is provided in [28] and [29].…”

“…Dividing the number of successfully forwarded messages to the total number of messages A sent to B for forwarding, a trust value regarding forwarding is reached. Each trust metric targets the detection of one (or more) routing attack(s), as explained in [28] and will also be shown in the performance evaluation section. For example, the first trust metric (forwarding) allows for the detection of black hole and grey hole attackers, i.e.…”