Abstract
Security techniques like cryptography and authentication can fail to protect a network once a node is compromised. Hence, trust establishment continuously monitors and evaluates node behavior to detect malicious and compromised nodes. However, just like other security schemes, trust establishment is also vulnerable to attack. Moreover, malicious nodes might misbehave intelligently to trick trust establishment schemes. Unfortunately, attack-resistance and robustness issues with trust establishment schemes have not received much attention from the research community. Considering the vulnerability of trust establishment to different attacks and the unique features of sensor nodes in wireless sensor networks, we propose a lightweight and robust trust establishment scheme. The proposed trust scheme is lightweight thanks to a simple trust estimation method. The comprehensiveness and flexibility of the proposed trust estimation scheme make it robust against different types of attack and misbehavior. Performance evaluation under different types of misbehavior and on-off attacks shows that the detection rate of the proposed trust mechanism is higher and more stable compared to other trust mechanisms.
Highlights
Trust establishment is one of the more recent research trends in many fields, such as web-based services, e-commerce, peer-to-peer networks, and wireless networks
A malicious node demonstrates persistently bad behavior, and the rate of misbehavior can be either significant or insignificant. This kind of assumption is important in wireless sensor networks (WSNs), because research studies show that a sensor node often becomes stuck malfunctioning [8]
We propose a robust and lightweight trust mechanism for WSNs
Summary
Trust establishment is one of the more recent research trends in many fields, such as web-based services, e-commerce, peer-to-peer networks, and wireless networks. Groups of malicious nodes can organize collaborative attacks against trust establishment This implies that attack and misbehavior type, intensity, strategy, frequency, etc., can vary according to the application. Malicious nodes can persistently and intentionally maintain fewer bad behaviors compared to number of good behaviors, so they are not detected while slowly damaging the network This issue is not addressed in previous research. Depending on the performance of the node, trust estimation adapts different equations to estimate trust in order to mitigate the effects of on-off attacks Another important feature of the proposed scheme is that it can differentiate between legitimate and malicious nodes. Comprehensive performance evaluation results show that the proposed scheme can more efficiently detect different misbehavior and on-off attacks more efficiently, compared to other methods.
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