Imperial College London

Professor Emil Lupu

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



e.c.lupu Website




564Huxley BuildingSouth Kensington Campus






BibTex format

author = {Illiano, V and Lupu, E and Muñoz-González, L and Paudice, AP},
doi = {10.1145/3176621},
journal = {ACM Transactions on Sensor Networks},
title = {Determining Resilience Gains from Anomaly Detection for Event Integrity in Wireless Sensor Networks},
url = {},
volume = {14},
year = {2018}

RIS format (EndNote, RefMan)

AB - 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.
AU - Illiano,V
AU - Lupu,E
AU - Muñoz-González,L
AU - Paudice,AP
DO - 10.1145/3176621
PY - 2018///
SN - 1550-4859
TI - Determining Resilience Gains from Anomaly Detection for Event Integrity in Wireless Sensor Networks
T2 - ACM Transactions on Sensor Networks
UR -
UR -
VL - 14
ER -