@conference {
	title = {Robust State Estimation Under False Data Injection in Distributed Sensor Networks},
	author = {Zheng, Shanshan., Jiang, Tao., Baras, John S.},
	year = {2010},
	month = {12/2010},
	abstract = {Distributed sensor networks have been widely employed to monitor and protect critical infrastructure assets. The network status can be estimated by centralized state estimation using coordinated data aggregation or by distributed state estimation, where nodes only exchange information locally to achieve enhanced scalability and adaptivity to network dynamics. One important property of state estimation is robustness against false data injection from sensors compromised by attackers. Different from most existing works in the literature that focus on centralized state estimation, we propose two novel robust distributed state estimation algorithms against false data injection. They are built upon an existing distributed Kalman filtering algorithm. In the first algorithm, we use variational Bayesian learning to estimate attack parameters and achieve performance similar to a centralized majority voting rule, without causing extra communication overhead. In the second algorithm, we introduce heterogeneity into the network by utilizing a subset of pre-trusted nodes to achieve performance better than majority voting. We show that as long as there is a path connecting each node to some of the pre-trusted nodes, the attackers can not subvert the network. Experimental results demonstrate the effectiveness of our proposed schemes.},
	address = {Miami, Florida},
	booktitle = {IEEE Globecom 2010},
	pages = {pp.1-5}
}
@conference {
	title = {Trust-aware State Estimation Under False Data Injection In Distributed Sensor Networks},
	author = {Zheng, Shanshan., Jiang, Tao., Baras, John S.},
	year = {2010},
	month = {09/2010},
	abstract = {Distributed sensor networks have been widely employed to monitor and protect critical infrastructure assets. The network status can be estimated by centralized state estimation using coordinated data aggregation or by distributed state estimation, where nodes only exchange information locally to achieve enhanced scalability and adaptivity to network dynamics. One important property of state estimation is robustness against false data injection from sensors compromised by attackers. Different from most existing works in the literature that focus on centralized state estimation, we propose two novel robust distributed state estimation algorithms against false data injection. They are built upon an existing distributed Kalman filtering algorithm. In the first algorithm, we use variational Bayesian learning to estimate attack parameters and achieve performance similar to a centralized majority voting rule, without causing extra communication overhead. In the second algorithm, we introduce heterogeneity into the network by utilizing a subset of pre-trusted nodes to achieve performance better than majority voting. We show that as long as there is a path connecting each node to some of the pre-trusted nodes, the attackers can not subvert the network. Experimental results demonstrate the effectiveness of our proposed schemes.},
	address = {Austin, Texas},
	booktitle = {TECHCON 2010},
	pages = {pp. 1-4}
}
@conference {
	title = {A Trust Based Distributed Kalman Filtering Approach for Mode Estimation in Power Systems},
	author = {Jiang, Tao., Matei, Ion., Baras, John S.},
	year = {2010},
	month = {04/2010},
	abstract = {We consider distributed mode estimation in power systems. The measurements are observed by PMUs (Power Management Units). We introduce a novel model of trust, using weights on the graph links and nodes that represent the networked PMUs. We describe two algorithms that intergarte distributed Kalman filtering with these trust weights. We consider two interpretations of these trust weights as information accuracy and reliability. We show that by appropriate use of these weights from the distributed estimation algorithm avoids using information from untrusted PMUs. Simulation experiments further demonstrate the behavior of these algorithms.},
	address = {Stockholm, Sweden},
	booktitle = {First Workshop on Secure Control Systems (SCS) as part of CPSWeek 2010},
	pages = {pp. 1-6}
}
@conference {
	title = {Compositional Framework for System Security with Multi-metric Optimization"},
	author = {Baras, John S., Jiang, Tao., Somasundaram, Kiran K., Ivanov, Vladimir I.},
	year = {2010},
	month = {03/2010},
	abstract = {The objective of the proposed research is to develop a compositional framework and methods for~performance vs security tradeoffs via multi-criteria optimization of system security, and therefore it aligns~with problem statement 2: {\textquotedblleft}Security Framework.{\textquotedblright} We propose to study a top-down approach ~for~optimizing (1) the security metric(s) of a composite system viewed as a bivariate function of its~subsystems{\textquoteright} security and performance metrics under given constraints and (2) the composition of~subsystems that provides higher system security than that of the composition of the same subsystems~driven solely by performance maximization. In this way, we reconcile the trade-off between security and~performance by introducing a holistic, multi-objective solution that satisfies both the performance and the~security requirements for the system, by overcoming the problem of compartmentalized security solutions~and thus achieving the {\textquotedblleft}best value{\textquotedblright} of the system. Our framework delineates methods for achieving~provably secure composition of secure subsystems and system components {\textendash} a critical problem area~where almost all current results demonstrate infeasibility of achieving compositional security.},
	address = {Washington DC},
	booktitle = {SERC Security Workshop}
}
