Lanchao Liu

Detecting False Data Injection Attacks on Power Grid by Sparse Optimization

State estimation in electric power grid is vulnerable to false data injection attacks, and diagnosing such kind of malicious attacks has significant impacts on ensuring reliable operations for power systems. In this paper, the false data detection problem is viewed as a matrix separation problem. By noticing the intrinsic low dimensionality of temporal measurements of power grid states as well as the sparse nature of false data injection attacks, a novel false data detection mechanism is proposed based on the separation of nominal power grid states and anomalies. Two methods, the nuclear norm minimization and low rank matrix factorization, are presented to solve this problem. It is shown that proposed methods are able to identify proper power system operation states as well as detect the malicious attacks, even under the situation that collected measurement data is incomplete. Numerical simulation results both on the synthetic and real data validate the effectiveness of the proposed mechanism.

Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid

Aging power industries, together with the increase in demand from industrial and residential customers, are the main incentive for policy makers to define a road map to the next-generation power system called the smart grid. In the smart grid, the overall monitoring costs will be decreased, but at the same time, the risk of cyber attacks might be increased. Recently, a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine-learning-based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a distributed support vector machine (SVM). The design of the distributed SVM is based on the alternating direction method of multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects the deviation in measurements. In both methods, principal component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on IEEE standard test systems demonstrate the effectiveness of both schemes.

Detection of false data injection in power grid exploiting low rank and sparsity

Smart grids are vulnerable to cyber attacks because of the inevitable coupling between cyber and physical operations. Diagnosing such malicious false data attack has significant importance to ensure reliable operations of power grids. This task is challenging, however, when attackers inject bad data into power systems that are able to circumvent the traditional maximum residual detection method. By noticing the intrinsic low rank structure of temporal erroneous-free measurements of power grid as well as sparse nature of observable malicious attacks, we formulate the false data detection problem as low-rank matrix recovery and completion problem, which is solved by convex optimization that minimizes a combination of the nuclear norm and the l1 norm. To efficiently solve this mixed-norm optimization, the method of augmented Lagrange multipliers is applied, which offers provable optimality and convergence rate. Numerical simulation results both on the synthetic and real data validate the effectiveness of the proposed mechanism.

Collaborative Compressive Sensing Based Dynamic Spectrum Sensing and Mobile Primary User Localization in Cognitive Radio Networks

In wideband cognitive radio (CR) networks, spectrum sensing is one of the key issues that enable the whole network functionality. Collaborative spectrum sensing among the cognitive radio nodes can greatly improve the sensing performance, and is also able to obtain the location information of primary radios (PRs). Most existing work merely studies the cognitive radio networks with static PRs, yet how to deal with the situations for mobile PRs remains less addressed. In this paper, we propose a collaborative compressive sensing based approach to estimate both the power spectrum and locations of the PRs by exploiting the sparsity facts: the relative narrow band nature of the transmitted signals compared with the broad bandwidth of available spectrum and the mobile PRs located sparsely in the operational space. To effectively track mobile PRs, we implement a Kalman filter using the current estimations to update the location information. To handle dynamics in spectrum usage, a dynamic compressive spectrum sensing algorithm is proposed. Joint consideration of the above two techniques is also investigated. Simulation results validate the effectiveness and robustness of the proposed approach.

A distributed ADMM approach for mobile data offloading in software defined network

Mobile data offloading has been introduced to alleviate the congestion of cellular networks and to improve the quality of service for mobile end users. This paper presents a distributed mechanism for mobile data offloading in software defined network (SDN) at the network edge. In SDN, the data traffic of base stations (BSs) can be dynamically offloaded to access points (APs), which is enabled by the SDN controller. The SDN controller formulates a revenue maximization problem to optimize the data offloading decision, and solves the problem in a fully distributed fashion. The proposed mechanism is based on the proximal Jacobian multi-block alternating direction method of multipliers (ADMM). BSs and APs perform the offloading decision update concurrently, and are coordinated by the SDN controller through dual variables to reach a consensus on the offloading demand and supply. Numerical simulations validate the effectiveness of the proposed algorithm.

A distribute parallel approach for big data scale optimal power flow with security constraints

This paper presents a mathematical optimization framework for security-constrained optimal power flow (SCOPF) computations. The SCOPF problem determines the optimal control of power systems under constraints arising from a set of postulated contingencies. This problem is challenging due to the significantly large problem size, the stringent real-time requirement and the variety of numerous post-contingency states. In order to solve the resultant big data scale optimization problem with manageable complexity, the alternating direction method of multipliers (ADMM) is utilized. The SCOPF is decomposed into independent subproblems correspond to each individual pre-contingency and post-contingency case. Those subproblems are solved in parallel on distributed nodes and coordinated through dual (prices) variables. As a result, the algorithm is implemented in a distributive and parallel fashion. Numerical tests validate the effectiveness of the proposed algorithm.

Sampling spectrum occupancy data over random fields: A matrix completion approach

The performance of cognitive radio networks is fundamentally determined by the availability of spectrum resources. Detailed measurement campaigns are needed to collect the spectrum occupancy data to obtain a deeper understanding of the spectrum usage characteristics in cognitive radio networks. This approach, however, is usually inefficient due to the ignorance of the spatial, temporal and spectral correlations of spectrum occupancies, and unpractical because of the geographical and hardware limitations of the cognitive radio nodes. In this paper, we apply the theory of random fields to model the spatial-temporal correlated spectrum usage data, using the two dimensional Ising model and the Metropolis-Hastings algorithm respectively. To efficiently obtain the spectrum occupancy, we adopt the matrix completion technique that leverages the low-rank structure of the data matrix to recover the original data from limited measurements. Simulation results validate effectiveness of the proposed algorithm.

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs’ idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs’ participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.

Incentive mechanism in crowdsourcing with moral hazard

With the widely adoption of smart mobile devices, there is a rapidly development of location based services. One key feature in providing the service is the crowdsourcing in which the principal obtains essential data from a large group of users, and inversely sharing the data based service with everyone for free. In this paper, we investigate the problem of how to provide continuous incentives for users to participate in the crowdsourcing activity, which can be referred to the moral hazard problem in the contract theory. First, a performance related incentive mechanism is proposed. Then, the utility maximization problem of the principal is formulated, under the constraint that each user maximizes its own utility by choosing the optimal effort in the crowdsourcing activity. Finally, the numerical results show that by using the proposed incentive mechanism, the users obtains the continuous incentives to participate in the crowdsourcing activity, and the principal successfully maximize the utilities.

Spectrum Sensing and Primary User Localization in Cognitive Radio Networks via Sparsity

The theory of compressive sensing (CS) has been employed to detect available spectrum resource in cognitive radio (CR) networks recently. Capitalizing on the spectrum resource underutilization and spatial sparsity of primary user (PU) locations, CS enables the identification of the unused spectrum bands and PU locations at a low sampling rate. Although CS has been studied in the cooperative spectrum sensing mechanism in which CR nodes work collaboratively to accomplish the spectrum sensing and PU localization task, many important issues remain unsettled. Does the designed compressive spectrum sensing mechanism satisfy the Restricted Isometry Property, which guarantees a successful recovery of the original sparse signal? Can the spectrum sensing results help the localization of PUs? What are the characteristics of localization errors? To answer those questions, we try to justify the applicability of the CS theory to the compressive spectrum sensing framework in this paper, and propose a design of PU localization utilizing the spectrum usage information. The localization error is analyzed by the Cramér-Rao lower bound, which can be exploited to improve the localization performance. Detail analysis and simulations are presented to support the claims and demonstrate the efficacy and efficiency of the proposed mechanism. Received on 30 September 2013; accepted on 14 November 2013; published on 11 April 2014