Showing papers by "Gyorgy Dan published in 2020"

Computation Offloading Scheduling for Periodic Tasks in Mobile Edge Computing

Abstract: Motivated by various delay sensitive applications, we address the problem of coordinating the offloading decisions of wireless devices that periodically generate computationally intensive tasks. We consider autonomous devices that aim at minimizing their own cost by choosing when to perform their tasks and whether or not to offload their tasks to an edge cloud through one of the multiple wireless links. We develop a game theoretical model of the problem, prove the existence of pure strategy Nash equilibria and propose a polynomial complexity algorithm for computing an equilibrium. Furthermore, we characterize the structure of the equilibria, and by providing an upper bound on the price of anarchy of the game we establish an asymptotically tight bound on the approximation ratio of the proposed algorithm. Our simulation results show that the proposed algorithm achieves significant performance gain compared to uncoordinated computation offloading at a computational complexity that is on average linear in the number of devices.

A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

Abstract: Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and non-video traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.

Feasibility of Time-Synchronization Attacks Against PMU-Based State Estimation

Abstract: The emerging measurement technology of phasor measurement units (PMUs) makes it possible to estimate the state of electrical grids in real time, thus opening the way to new protection and control applications. PMUs rely on precise time synchronization; therefore, they are vulnerable to time-synchronization attacks (TSAs), which alter the measured voltage and current phases. In particular, undetectable TSAs pose a significant threat as they lead to an incorrect but credible estimate of the system state. Prior work has shown that such attacks exist against pairs of PMUs, but they do not take into consideration the clock adjustment performed by the clock servo, which can modify the attack angles and make the attacks detectable. This cannot be easily addressed with the existing attacks, as the undetectable angle values form a discrete set and cannot be continuously adjusted as would be required to address the problems posed to the attacker by the clock servo. Going beyond prior work, this article first shows how to perform undetectable attacks against more than two PMUs, so that the set of undetectable attacks forms a continuum and supports small adjustments. Second, it shows how an attacker can anticipate the operation of the clock servo while achieving her attack goal and remaining undetectable. Third, this article shows how to identify vulnerable sets of PMUs. Numerical results on the 39-bus IEEE benchmark system illustrate the feasibility of the proposed attack strategies.

Model-Based and Data-Driven Detectors for Time Synchronization Attacks Against PMUs

Abstract: Precise time synchronization of Phasor Measurement Units (PMUs) is critical for monitoring and control of smart grids. Thus, time synchronization attacks (TSAs) against PMUs pose a severe threat to smart grid security. In this paper we present an approach for detecting TSAs based on the interaction between the time synchronization system and the power system. We develop a phasor measurement model and use it to derive an accurate closed form expression for the correlation between the frequency adjustments made by the PMU clock and the resulting change in the measured phase angle, without an attack. We then propose one model-based and three data-driven TSA detectors that exploit the change in correlation due to a TSA. Using extensive simulations, we evaluate the proposed detectors under different strategies for implementing TSAs, and show that the proposed detectors are superior to state-of-the-art clock frequency anomaly detection, especially for unstable clocks.

Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection

Abstract: Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which ...

Passive Fault-tolerant Estimation under Strategic Adversarial Bias

Abstract: This paper is concerned with the problem of fault-tolerant estimation in cyber-physical systems. In cyber-physical systems, such as critical infrastructures, networked embedded sensors are widely used for monitoring and can be exploited by an adversary to deceive the control center by modifying measured values. The deception is modeled as a bias; i.e., there is a misalignment between the objective functions of the control center and the adversarial sensor. Different from previous studies, a Stackelberg equilibrium of a cheap talk setup is adapted to the attacker-defender game setting for the first time. That is, the defender (control center), as a receiver, is the leader, and the attacker (adversarial sensor), as a transmitter, is the follower. The equilibrium strategies and the associated costs are characterized for uniformly distributed variables and quadratic objective functions, and an analysis on the uniqueness of the equilibrium is provided. It is shown that the attacker and defender costs at the equilibrium are increasing with the bias and decreasing with the number of quantization levels. Our results surprisingly show that, under certain conditions, the attacker prefers a public bias rather than a private one.

Semi-Persistent Scheduling for 5G Downlink Based on Short-Term Traffic Prediction

Abstract: Efficient communication and computing resource allocation is becoming a fundamental issue in wireless networks. Efficiency is most often defined in terms of throughput, utilization and spectral efficiency, while the required computational effort is often overlooked. In this paper, we focus on efficient and computationally lightweight downlink scheduling, and we propose a semi-persistent scheduler based on adaptive short term traffic prediction. We evaluate the performance of the proposed scheduler in terms of throughput, fairness, latency, and scheduling complexity. Our numerical results show that scheduling with prediction is a promising approach in improving network performance. The proposed semi-persistent scheduler performs equally well in terms of throughput, fairness, and latency as traditional proportional-fair scheduling, but at a significantly reduced computational cost.

A Comparison of Machine and Statistical Time Series Learning for Encrypted Traffic Prediction

Abstract: This paper studies the utilization of machine and statistical learning methods for predicting encrypted user traffic. To this end, a reference system model and performance metrics for traffic prediction have been defined for enabling on-line training. Based on a collection of representative traffic data sets including various video and web traffic, two different classes of predictors have been evaluated. Our results show that very good prediction performance can be achieved using long short-term memory (LSTM) recurrent neural networks at the price of a significant computational cost for training.

Network-aware Mitigation of Undetectable PMU Time Synchronization Attacks

Abstract: Time synchronization attacks are an emerging threat to many future smart grid applications, their mitigation is thus of utmost importance. In this paper we consider the problem of mitigating attacks that are undetectable by state-of-the-art power system state estimation, in precision time protocol networks. We formulate our problem as an integer linear program and show that it is NP-hard. We then provide a polynomial time approximation algorithm through a reduction from the group Steiner tree problem. We evaluate the performance of the proposed algorithm through extensive simulations compared to a greedy heuristic. Our results show that the approximation algorithm performs within a factor 1.8 of the optimal solution for synthetic topologies, while the greedy algorithm performs even better. On IEEE benchmark power systems the approximation algorithm performs within a factor 1.1 of the optimal solution, as good as the greedy heuristic.

A Game Theoretic Analysis of LQG Control under Adversarial Attack

Abstract: Motivated by recent works addressing adversarial attacks on deep reinforcement learning, a deception attack on linear quadratic Gaussian control is studied in this paper. In the considered attack model, the adversary can manipulate the observation of the agent subject to a mutual information constraint. The adversarial problem is formulated as a novel dynamic cheap talk game to capture the strategic interaction between the adversary and the agent, the asymmetry of information availability, and the system dynamics. Necessary and sufficient conditions are provided for subgame perfect equilibria to exist in pure strategies and in behavioral strategies; and characteristics of the equilibria and the resulting control rewards are given. The results show that pure strategy equilibria are informative, while only babbling equilibria exist in behavioral strategies. Numerical results are shown to illustrate the impact of strategic adversarial interaction.

Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection

Abstract: Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the Joint Slice Selection and Edge Resource Management(JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We provide extensive simulation results to show that network slicing can improve the system performance compared to no slicing and that the proposed solution can achieve significant gains compared to the equal slicing policy. Our results also show that the computational complexity of the proposed algorithm is approximately linear in the number of devices.

A Game Theoretic Analysis of LQG Control under Adversarial Attack

Abstract: Motivated by recent works addressing adversarial attacks on deep reinforcement learning, a deception attack on linear quadratic Gaussian control is studied in this paper. In the considered attack model, the adversary can manipulate the observation of the agent subject to a mutual information constraint. The adversarial problem is formulated as a novel dynamic cheap talk game to capture the strategic interaction between the adversary and the agent, the asymmetry of information availability, and the system dynamics. Necessary and sufficient conditions are provided for subgame perfect equilibria to exist in pure strategies and in behavioral strategies; and characteristics of the equilibria and the resulting control rewards are given. The results show that pure strategy equilibria are informative, while only babbling equilibria exist in behavioral strategies. Numerical results are shown to illustrate the impact of strategic adversarial interaction.

Formalising the Impact of Security Attacks on IoT Safety

Abstract: Modern safety-critical systems become increasingly networked and interconnected. Often the communication between the system components utilises the protocols similar to the standard Internet Protocol (IP). In particular, such protocols are used for communication between smart sensors and controller. While offering advanced capabilities such as remote diagnostics and maintenance, this also make safety-critical systems susceptible to the attacks implementable against IP-based systems. In this paper, we propose an approach to specifying a generic IP-based networked control system and formalising its security properties. We use the Event-B framework to formally analyse the impact of security attacks on safety properties of the system.

Dynamic Migration of Real-Time Traffic Flows in SDN-Enabled Networks

Abstract: In this paper, we investigate the problem of dynamic migration for realtime traffic flows, which consists in accommodating new flows at runtime in SDN-enabled networks. We show results for two algorithms that can calculate direct and indirect flow migrations at runtime. Numerical results obtained on a FatTree network topology show that flow migration is typically required for networks with a modest number of flows, while direct flow migration is possible in about 60% of the cases.

Caching Policies over Unreliable Channels

Abstract: Recently, there has been substantial progress in the formal understanding of how caching resources should be allocated when multiple caches each deploy the common LRU policy. Nonetheless, the role played by caching policies beyond LRU in a networked setting where content may be replicated across multiple caches and where channels are unreliable is still poorly understood. In this paper, we investigate this issue by first analyzing the cache miss rate in a system with two caches of unit size each, for the LRU, and the LFU caching policies, and their combination. Our analytical results show that joint use of the two policies outperforms LRU, while LFU outperforms all these policies whenever resource pooling is not optimal. We provide empirical results with larger caches to show that simple alternative policies, such as LFU, provide superior performance compared to LRU even if the space allocation is not fine tuned. We envision that fine tuning the cache space used by such policies may lead to promising additional gains.