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We consider a training data collection mechanism wherein, instead of annotating each training instance with a class label, additional features drawn from a known class-conditional distribution are acquired concurrently. Considering true labels as latent variables, a maximum likelihood approach is proposed to train a classifier based on these unlabeled training data. Furthermore, the case of correlated...
We study sparse gross error correction for state estimation in a non-linear sensing system. We consider a practical assumption that gross errors are sparse, and their locations tend to be invariant over a few consecutive measurement periods. Under the assumption, a robust state estimation and error correction algorithm using multiple measurement vectors is proposed based on local linear approximation...
Recognizing and localizing a recurring pattern is a problem with a variety of applications such as classification and localization of home appliances from their activation signals and estimating the relative alignment between records of a natural repetitive electrocardiography (ECG) signals in Bio-medical data. Most common approaches for recognizing a recurring pattern are generative and focus on...
In this paper, we present feasibility study of sparse error correction in power system measurements. Legacy bad data detection mechanisms have been shown to be prone to make erroneous decisions when elaborately designed errors are injected by cyber attacks. In order to effectively handle such gross errors, sparse error correction framework has been suggested in the literature. For proper utilization...
This paper investigates the potential impacts of load oscillating attacks in a microgrid to the stability of the main power grid. The adversary is assumed to be able to control switches within compromised smart meters and thus is able to dynamically connect or disconnect the corresponding loads within the microgrid. Using the commercial PSS/e time-domain simulator with the IEEE Reliability Test System...
Random matrix theory is applied in areas of signal processing, communications, and machine learning. One aspect of random matrix theory involves the study of the eigenvalues of random matrices. For example, in communications, the eigenvalues associated with a channel matrix are used in the analysis of channel capacity and in compressive sensing the eigenvalues of sub-matrices of the sampling matrix...
In synthesis dictionary learning, data is compactly represented as sparse combination over a dictionary. In analysis dictionary learning, the dictionary directly transforms the data to produce a sparse outcome. In this paper, we concentrate on the problem of analysis dictionary learning under the weak supervision setting. We introduce a discriminative probabilistic model and present a novel approach...
In this paper, we examine an approach for robust state estimation that exploits the sparse nature of gross errors in sensor system measurements and study the feasibility of gross error identification. Under the practical assumption that potential locations of gross errors remain fixed during multiple measurement periods, gross error correction based on multiple measurement vectors is proposed. Our...
A dynamic data attack on a power system aimed at making the real time economic dispatch infeasible is considered. As a man-in-the-middle attack, the attack modifies part of sensor measurements such that the control center is misled with an incorrect system state estimate, which affects the computation of real time economic dispatch. Two attack mechanisms are considered. The first is an opportunistic...
The problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future...
A data framing attack is presented to exploit the bad data detection and identification mechanisms at a typical ISO/RTO control center. In particular, the proposed attack frames normal meters as sources of bad data and causes the control center to remove useful measurements from the framed meters. The proposed attack uses subspace information of power system measurements; neither the network topology...
Cyber attacks on the SCADA system can mislead the control center to produce incorrect state and topology estimate. If undetected, state and topology attacks can have detrimental impacts on the real-time operation of a power system. The problem of placing secure phasor measurement units (PMUs) to detect such attacks is considered. It is shown that any state or topology attack is detectable if and only...
Cyber attacks on a smart grid aiming at misleading the control center with incorrect topology information are considered. In such attacks, an adversary intercepts network and meter data from the remote terminal units, modifies part of them, and forwards the modified data to the control center. A necessary and sufficient condition for an undetectable topology attack is presented, and an undetectable...
The problem of detecting the presence of time-varying flows in multi-hop wireless networks is considered. In particular, from transmission timing measurements, a test is constructed to determine whether there is a flow of data packets between a pair of nodes. It is assumed that the packet flows may have time-varying (piecewise constant) flow rates. First, a timing-based detector is proposed to detect...
The problem of detecting packet flows between two nodes in a wireless network is considered. Especially, the transmission timings of two nodes are recorded, and their transmission rates can be time-varying (piecewise constant). Based on the timing measurements, our objective is to detect the presence of packet flows between them. Two different scenarios are considered; the first is that a flow may...
In mobile ad hoc networks (MANETs), timing information is easily available due to the use of a shared medium, even when the traffic is encrypted. This paper addresses how such timing information can be used for detecting packet forwarding activities in MANETs. Our results depend in part on the previous results on unidirectional flow detection. We first provide further analysis for the unidirectional...
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