Biblio

This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

This paper sheds light on the collaborative efforts in restoring cyber and physical subsystems of a modern power distribution system after the occurrence of an extreme weather event. The extensive cyber-physical interdependencies in the operation of power distribution systems are first introduced for investigating the functionality loss of each subsystem when the dependent subsystem suffers disruptions. A resilience index is then proposed for measuring the effectiveness of restoration activities in terms of restoration rapidity. After modeling operators' decision making for economic dispatch as a second-order cone programming problem, this paper proposes a heuristic approach for prioritizing the activities for restoring both cyber and physical subsystems. In particular, the proposed heuristic approach takes into consideration of cyber-physical interdependencies for improving the operation performance. Case studies are also conducted to validate the collaborative restoration model in the 33-bus power distribution system.

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-utility tradeoff. In this study we propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. We obtain a sufficient condition for the convergence of R-ADMM and provide the privacy analysis based on objective perturbation.

This paper studies secure communication from a multi-antenna transmitter to a single-antenna receiver in the presence of multiple multi-antenna eavesdroppers, considering constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers. The robust joint optimal beamforming (RJOBF) of secret signal and artificial noise (AN) is designed to minimize transmit power while estimation errors of channel state information (CSI) for wiretap channels are taken into consideration. The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs) along with semidefinite relaxation (SDR) technique. The simulation results illustrate that our proposed RJOBF is efficient for power saving in security communication.

In this paper, we present an algorithm for estimating the state of the power grid following a cyber-physical attack. We assume that an adversary attacks an area by: (i) disconnecting some lines within that area (failed lines), and (ii) obstructing the information from within the area to reach the control center. Given the phase angles of the buses outside the attacked area under the AC power flow model (before and after the attack), the algorithm estimates the phase angles of the buses and detects the failed lines inside the attacked area. The novelty of our approach is the transformation of the line failures detection problem, which is combinatorial in nature, to a convex optimization problem. As a result, our algorithm can detect any number of line failures in a running time that is independent of the number of failures and is solely dependent on the size of the network. To the best of our knowledge, this is the first convex relaxation for the problem of line failures detection using phase angle measurements under the AC power flow model. We evaluate the performance of our algorithm in the IEEE 118- and 300-bus systems, and show that it estimates the phase angles of the buses with less that 1% error, and can detect the line failures with 80% accuracy for single, double, and triple line failures.

Machine learning and data mining algorithms typically assume that the training and testing data are sampled from the same fixed probability distribution; however, this violation is often violated in practice. The field of domain adaptation addresses the situation where this assumption of a fixed probability between the two domains is violated; however, the difference between the two domains (training/source and testing/target) may not be known a priori. There has been a recent thrust in addressing the problem of learning in the presence of an adversary, which we formulate as a problem of domain adaption to build a more robust classifier. This is because the overall security of classifiers and their preprocessing stages have been called into question with the recent findings of adversaries in a learning setting. Adversarial training (and testing) data pose a serious threat to scenarios where an attacker has the opportunity to ``poison'' the training or ``evade'' on the testing data set(s) in order to achieve something that is not in the best interest of the classifier. Recent work has begun to show the impact of adversarial data on several classifiers; however, the impact of the adversary on aspects related to preprocessing of data (i.e., dimensionality reduction or feature selection) has widely been ignored in the revamp of adversarial learning research. Furthermore, variable selection, which is a vital component to any data analysis, has been shown to be particularly susceptible under an attacker that has knowledge of the task. In this work, we explore avenues for learning resilient classification models in the adversarial learning setting by considering the effects of adversarial data and how to mitigate its effects through optimization. Our model forms a single convex optimization problem that uses the labeled training data from the source domain and known- weaknesses of the model for an adversarial component. We benchmark the proposed approach on synthetic data and show the trade-off between classification accuracy and skew-insensitive statistics.

Friendly jamming is a physical layer security technique that utilizes extra available nodes to jam any eavesdroppers. This paper considers the use of additional available nodes as friendly jammers in order to improve the security performance of a route through a wireless area network. One of the unresolved technical challenges is the combining of security metrics with typical service quality metrics. In this context, this paper considers the problem of routing through a D2D network while jointly minimizing the secrecy outage probability (SOP) and connection outage probability (COP), using friendly jamming to improve the SOP of each link. The jamming powers are determined to place nulls at friendly receivers while maximizing the power to eavesdroppers. Then the route metrics are derived, and the problem is framed as a convex optimization problem. We also consider that not all network users equally value SOP and COP, and so introduce an auxiliary variable to tune the optimization between the two metrics.

We propose a distributed continuous-time algorithm to solve a network optimization problem where the global cost function is a strictly convex function composed of the sum of the local cost functions of the agents. We establish that our algorithm, when implemented over strongly connected and weight-balanced directed graph topologies, converges exponentially fast when the local cost functions are strongly convex and their gradients are globally Lipschitz. We also characterize the privacy preservation properties of our algorithm and extend the convergence guarantees to the case of time-varying, strongly connected, weight-balanced digraphs. When the network topology is a connected undirected graph, we show that exponential convergence is still preserved if the gradients of the strongly convex local cost functions are locally Lipschitz, while it is asymptotic if the local cost functions are convex. We also study discrete-time communication implementations. Specifically, we provide an upper bound on the stepsize of a synchronous periodic communication scheme that guarantees convergence over connected undirected graph topologies and, building on this result, design a centralized event-triggered implementation that is free of Zeno behavior. Simulations illustrate our results.

We propose a distributed continuous-time algorithm to solve a network optimization problem where the global cost function is a strictly convex function composed of the sum of the local cost functions of the agents. We establish that our algorithm, when implemented over strongly connected and weight-balanced directed graph topologies, converges exponentially fast when the local cost functions are strongly convex and their gradients are globally Lipschitz. We also characterize the privacy preservation properties of our algorithm and extend the convergence guarantees to the case of time-varying, strongly connected, weight-balanced digraphs. When the network topology is a connected undirected graph, we show that exponential convergence is still preserved if the gradients of the strongly convex local cost functions are locally Lipschitz, while it is asymptotic if the local cost functions are convex. We also study discrete-time communication implementations. Specifically, we provide an upper bound on the stepsize of a synchronous periodic communication scheme that guarantees convergence over connected undirected graph topologies and, building on this result, design a centralized event-triggered implementation that is free of Zeno behavior. Simulations illustrate our results.