Wireless Sensor Networks are widely advocated to monitor environmental parameters, structural integrity of the built environment and use of urban spaces, services and utilities. However, embedded sensors are vulnerable to compromise by external actors through malware but also through their wireless and physical interfaces. Compromised sensors can be made to report false measurements with the aim to produce inappropriate and potentially dangerous responses. Such malicious data injections can be particularly difficult to detect if multiple sensors have been compromised as they could emulate plausible sensor behaviour such as failures or detection of events where none occur. This survey reviews the related work on malicious data injection in wireless sensor networks, derives general principles and a classification of approaches within this domain, compares related studies and identifies areas that require further investigation.

Wireless Sensor Networks carry a high risk of being compromised, as their deployments are often unattended, physically accessible and the wireless medium is difficult to secure. Malicious data injections take place when the sensed measurements are maliciously altered to trigger wrong and potentially dangerous responses. When many sensors are compromised, they can collude with each other to alter the measurements making such changes difficult to detect. Distinguishing between genuine and malicious measurements is even more difficult when significant variations may be introduced because of events, especially if more events occur simultaneously. We propose a novel methodology based on wavelet transform to detect malicious data injections, to characterise the responsible sensors, and to distinguish malicious interference from faulty behaviours. The results, both with simulated and real measurements, show that our approach is able to counteract sophisticated attacks, achieving a significant improvement over state-of-the-art approaches.

Wireless Sensor Networks (WSNs) are vulnerable and can be maliciously compromised, either physically or remotely, with potentially devastating effects. When sensor networks are used to detect the occurrence of events such as fires, intruders or heart-attacks, malicious data can be injected to create fake events and, thus, trigger an undesired response, or to mask the occurrence of actual events. We propose a novel algorithm to identify malicious data injections and build measurement estimates that are resistant to several compromised sensors even when they collude in the attack. We also propose a methodology to apply this algorithm in different application contexts and evaluate its results on three different datasets drawn from distinct WSN deployments. This leads us to identify different trade-offs in the design of such algorithms and how they are influenced by the application context. Index Terms-Security management, Ad-Hoc and sensor networks, Mining and statistical methodsThis is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication.The final version of record is available at http://dx.

Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack's chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system's resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement.

A estation and measurements inspection are di erent but complementary approaches towards the same goal: ascertaining the integrity of sensor nodes in wireless sensor networks. In this paper we compare the bene ts and drawbacks of both techniques and seek to determine how to best combine them. However, our study shows that no single solution exists, as each choice introduces changes in the measurements collection process, a ects the a estation protocol, and gives a di erent balance between the high detection rate of a estation and the low power overhead of measurements inspection. erefore, we propose three strategies that combine measurements inspection and a estation in di erent ways, and a way to choose between them based on the requirements of di erent applications. We analyse their performance both analytically and in a simulator. e results show that the combined strategies can achieve a detection rate close to a estation, in the range 96-99%, whilst keeping a power overhead close to measurements inspection, in the range 1-10%.