Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies

Data-Driven Quickest Change Detection

The quickest change detection (QCD) problem is to detect abrupt changes in a sensing environment as quickly as possible in real time while limiting the risk of false alarm. Statistical inference about the monitored stochastic process is performed through observations acquired sequentially over time. After each observation, QCD algorithm either stops and declares a change or continues to have a further observation in the next time interval. There is an inherent tradeoff between speed and accuracy in the decision making process. The design goal is to optimally balance the average detection delay and the false alarm rate to have a timely and accurate response to abrupt changes. The objective of this thesis is to investigate effective and scalable QCD approaches for real-world data streams. The classical QCD framework is model-based, that is, statistical data model is assumed to be known for both the pre- and post-change cases. However, real-world data often exhibit significant challenges for data modeling such as high dimensionality, complex multivariate nature, lack of parametric models, unknown post-change (e.g., attack or anomaly) patterns, and complex temporal correlation. Further, in some cases, data is privacy-sensitive and distributed over a system, and it is not fully available to QCD algorithm. This thesis addresses these challenges and proposes novel data-driven QCD approaches that are robust to data model mismatch and hence widely applicable to a variety of practical settings. In Chapter 2, online cyber-attack detection in the smart power grid is formulated as a partially observable Markov decision process (POMDP) problem based on the QCD framework. A universal robust online cyber-attack detection algorithm is proposed using the model-free reinforcement learning (RL) for POMDPs. In Chapter 3, online anomaly detection for big data streams is studied where the nominal (i.e., pre-change) and anomalous (i.e., post-change) high-dimensional statistical data models are unknown. A data-driven solution approach is proposed, where firstly a set of useful univariate summary statistics is computed from a nominal dataset in an offline phase and next, online summary statistics are evaluated for a persistent deviation from the nominal statistics. In Chapter 4, a generic data-driven QCD procedure is proposed, called DeepQCD, that learns the change detection rule directly from the observed raw data via deep recurrent neural networks. With sufficient amount of training data including both pre- and post-change samples, DeepQCD can effectively learn the change detection rule for all complex, high-dimensional, and temporally correlated data streams. Finally, in Chapter 5, online privacy-preserving anomaly detection is studied in a setting where the data is distributed over a network and locally sensitive to each node, and its statistical model is unknown. A data-driven differentially private distributed detection scheme is proposed, which infers network-wide anomalies based on the perturbed and encrypted statistics received from nodes. Furthermore, analytical privacy-security tradeoff in the network-wide anomaly detection problem is investigated

Telsiz konum belirleme sistemleri için karıştırıcı yerleştirme algoritmaları

Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2016.Includes bibliographical references (leaves 56-60).The optimal jammer placement problem is proposed and analyzed for wireless localization systems. In particular, the optimal location of a jammer node is obtained by maximizing the minimum of the Cram´er-Rao lower bounds (CRLBs) for a number of target nodes under location related constraints for the jammer node. For scenarios with more than two target nodes, theoretical results are derived to specify conditions under which the jammer node is located as close to a certain target node as possible, or the optimal location of the jammer node is determined by two of the target nodes. Also, explicit expressions are provided for the optimal location of the jammer node in the presence of two target nodes. In addition, in the absence of distance constraints for the jammer node, it is proved, for scenarios with more than two target nodes, that the optimal jammer location lies on the convex hull formed by the locations of the target nodes and is determined by two or three of the target nodes, which have equalized CRLBs. Numerical examples are presented to provide illustrations of the theoretical results in different scenarios. Furthermore, an iterative algorithm is proposed for numerically determining the optimal jammer location. At each iteration of the algorithm, the jammer node is moved one step along a straight line with the purpose of increasing the CRLB(s) of the target node(s) with the minimum CRLB in the system. It is shown that the algorithm converges almost surely to the optimal jammer location under certain conditions for an infinitesimally small step size in the absence of location constraints for the jammer node. Simulations illustrate the effectiveness of the proposed algorithm in finding the optimal jammer location and its superiority in terms of the computational complexity compared to the exhaustive search over all feasible locations.by Mehmet Necip Kurt.M.S