Research Article
Improving Robustness of a Popular Probabilistic Clustering Algorithm Against Insider Attacks
@INPROCEEDINGS{10.1007/978-3-030-63086-7_21, author={Sayed M. Saghaian N. E. and Tom La Porta and Simone Silvestri and Patrick McDaniel}, title={Improving Robustness of a Popular Probabilistic Clustering Algorithm Against Insider Attacks}, proceedings={Security and Privacy in Communication Networks. 16th EAI International Conference, SecureComm 2020, Washington, DC, USA, October 21-23, 2020, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2020}, month={12}, keywords={Probabilistic clustering algorithm Anomaly detection CUSUM test}, doi={10.1007/978-3-030-63086-7_21} }- Sayed M. Saghaian N. E.
Tom La Porta
Simone Silvestri
Patrick McDaniel
Year: 2020
Improving Robustness of a Popular Probabilistic Clustering Algorithm Against Insider Attacks
SECURECOMM
Springer
DOI: 10.1007/978-3-030-63086-7_21
Abstract
Many clustering algorithms for mesh, ad hoc and Wireless Sensor Networks have been proposed. Probabilistic approaches are a popular class of such algorithms. However, it is essential to analyze their robustness against security compromise. We study the robustness of EEHCA, a popular energy efficient clustering algorithm as an example of probabilistic class in terms of security compromise. In this paper, we investigate attacks on EEHCA through analysis and experimental simulations. We analytically characterize two different attack models. In the first attack model, the attacker aims to gain control over the network by stealing network traffic, or by disrupting the data aggregation process (integrity attack). In the second attack model, the inducement of the attacker is to abridge the network lifetime (denial of service attack). We assume the clustering algorithm is running periodically and propose a detection solution by exploiting Bernoulli CUSUM charts.
