Showing papers by "Gyorgy Dan published in 2022"

Detection and Localization of PMU Time Synchronization Attacks via Graph Signal Processing

Abstract: Time Synchronization Attacks (TSAs) against Phasor Measurement Units (PMUs) constitute a major threat to modern smart grid applications. By compromising the time reference of a set of PMUs, an attacker can change the phase angle of their measured phasors, with potentially detrimental impact on grid operation and control. Going beyond traditional residual-based techniques in detecting TSAs, in this paper we propose the use of Graph Signal Processing (GSP) to model the power grid so as to facilitate the detection and localization of TSAs. We analytically show that modeling the state of the power system as a low-pass graph signal can significantly improve the resilience of the grid against TSAs. We propose TSA detection and localization methods based on GSP, leveraging state-of-the-art machine learning algorithms. We provide empirical evidence for the efficiency of the proposed methods based on extensive simulations on five IEEE benchmark systems. In fact, our methods can detect at least 77% more TSAs of significant impact and localize an additional 70% of the attacked PMUs compared to state-of-the-art techniques.

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

Abstract: Abstract The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

Decentralized Convex Optimization for Joint Task Offloading and Resource Allocation of Vehicular Edge Computing Systems

Abstract: Vehicular Edge Computing (VEC) systems exploit resources on both vehicles and Roadside Units (RSUs) to provide services for real-time vehicular applications that cannot be completed in the vehicles alone. Two types of decisions are critical for VEC: one is for task offloading to migrate vehicular tasks to suitable RSUs, and the other is for resource allocation at the RSUs to provide the optimal amount of computational resource to the migrated tasks under constraints on response time and energy consumption. Most of the published optimization-based methods determine the optimal solutions of the two types of decisions jointly within one optimization problem at RSUs, but the complexity of solving the optimization problem is extraordinary, because the problem is not convex and has discrete variables. Meanwhile, the nature of centralized solutions requires extra information exchange between vehicles and RSUs, which is challenged by the additional communication delay and security issues. The contribution of this paper is to decompose the joint optimization problem into two decoupled subproblems: task offloading and resource allocation. Both subproblems are reformulated for efficient solutions. The resource allocation problem is simplified by dual decomposition and can be solved at vehicles in a decentralized way. The task offloading problem is transformed from a discrete problem to a continuous convex one by a probability-based solution. Our new method efficiently achieves a near-optimal solution through decentralized optimizations, and the error bound between the solution and the true optimum is analyzed. Simulation results demonstrate the advantage of the proposed approach.

A Scalable Formulation for Look-Ahead Security-Constrained Optimal Power Flow

Abstract: We consider the look-ahead security-const- rained optimal power flow (LASCOPF) problem under transmission line and generator contingencies. We first formulate LASCOPF under the $N-1$ contingency criterion ($\text{LASCOPF}_1$) using the dc power flow model. We observe that the number of decision variables in the comprehensive formulation increases quadratically with the number of look-ahead intervals, $T$, making the problem infeasible to solve for large $T$. To overcome this, we propose the reduced LASCOPF problem ($\text{LASCOPF-r}_1$) in which the number of decision variables increases only linearly with $T$. Thereafter, we prove that, barring borderline cases, if $\text{LASCOPF}_1$ is feasible then the optimal solutions of $\text{LASCOPF}_1$ and $\text{LASCOPF-r}_1$ are equivalent. We then extend our results to the $N-k$ contingency criterion ($\text{LASCOPF-ru}_k$) for any collection of $k$ contingencies, and we prove that the ordering of the contingencies does not affect the optimal solution. We then illustrate $\text{LASCOPF}_1$ on a simple 2-bus 2-generator system. We show the numerical benefits of the proposed $\text{LASCOPF-r}_1$ formulation on the IEEE 118-bus, the IEEE 300-bus, and the 2383-bus Polish systems.

Optimal Service Caching and Pricing in Edge Computing: a Bayesian Gaussian Process Bandit Approach

Abstract: Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading in a dynamic environment where Wirelesss Devices (WDs) can be active or inactive at any point in time. We model the problem as a single leader multiple-follower Stackelberg game, where the service operator is the leader and decides what applications to cache and how much to charge for their use, while the WDs are the followers and decide whether or not to offload their computations. We show that the WDs' interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that under perfect and complete information the equilibrium price of the service operator can be computed in polynomial time for any cache placement. For the incomplete information case, we propose a Bayesian Gaussian Process Bandit algorithm for learning an optimal price for a cache placement and provide a bound on its asymptotic regret. We then propose a Gaussian process approximation-based greedy heuristic for computing the cache placement. We use extensive simulations to evaluate the proposed learning scheme, and show that it outperforms state of the art algorithms by up to 50% at little computational overhead.

Inferring Class-Label Distribution in Federated Learning

Abstract: Federated Learning (FL) has become a popular distributed learning method for training classifiers by using data that are private to individual clients. The clients´ data are typically assumed to be confidential, but their heterogeneity and potential class-imbalance adversely impact the accuracy of the trained model. The class-imbalance may not be common knowledge or may even be confidential information itself. Thus, the inference of the class-label distribution of the training data is important both from a performance and from a privacy perspective. In this paper, we study the problem of class-label distribution inference from an adversarial perspective, based on model parameter updates sent to the parameter server. Firstly, we present conditions under which exact inference is possible. We then introduce four new methods to estimate class-label distribution in the general FL setting. We evaluate the proposed inference methods on four different datasets and our results show that they significantly outperform state of the art methods.

Analysing the Impact of Security Attacks on Safety Using SysML and Event-B

Abstract: AbstractSafety-critical control systems increasingly rely on networking technologies, which makes these systems vulnerable to cyber attacks that can potentially jeopardise system safety. To achieve safe- and secure- by-construction development, the designers should analyse the impact of security attacks already at the modelling stage. Since SysML is often used for modelling safety-critical systems, in this paper, we propose to integrate modelling in SysML and Event-B to enable reasoning about safety-security interactions at system modelling stage. Our approach combines the benefits of graphical modelling in SysML with the mathematical rigor of Event-B to visualise and formalise the analysis of the impact of security attacks on system safety.KeywordsSafety-security interactionsIntegrated approachFormal specification and verificationGraphical modelling

Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs

Abstract: This paper introduces a “green” routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can reduce inefficiency of equilibria.

Optimal Pricing for Service Caching and Task Offloading in Edge Computing

Abstract: Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading. We model the problem as a multiple-follower Stackelberg game, where the operator is the leader and decides what applications to cache and how much to charge for their use, while the wireless devices (WDs) are the followers and decide whether or not to offload their computations. We show that the WDs’ interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that the equilibrium price of the operator can be computed in polynomial time for any cache placement, and propose a greedy algorithm for computing the applications to be cached. We use extensive simulations to show that the proposed heuristic performs close to optimal at negligible computational overhead.

TECoSA – Trends, Drivers, and Strategic Directions for Trustworthy Edge Computing in Industrial Applications

Abstract: TECoSA – a university-based research center in collaboration with industry – was established early in 2020, focusing on Trustworthy Edge Computing Systems and Applications. This article summarizes and assesses the current trends and drivers regarding edge computing. In our analysis, edge computing provided by mobile network operators will be the initial dominating form of this new computing paradigm for the coming decade. These insights form the basis for the research agenda of the TECoSA center, highlighting more advanced use cases, including AR/VR/Cognitive Assistance, cyber-physical systems, and distributed machine learning. The article further elaborates on the identified strategic directions given these trends, emphasizing testbeds and collaborative multidisciplinary research.

An Active Learning Approach to Dynamic Alert Prioritization for Real-time Situational Awareness

Abstract: Real-time situational awareness (SA) plays an essential role in accurate and timely incident response. Maintaining SA is, however, extremely costly due to excessive false alerts generated by intrusion detection systems, which require prioritization and manual investigation by security analysts. In this paper, we propose a novel approach to prioritizing alerts so as to maximize SA, by formulating the problem as that of active learning in a hidden Markov model (HMM). We propose to use the entropy of the belief of the security state as a proxy for the mean squared error (MSE) of the belief, and we develop two computationally tractable policies for choosing alerts to investigate that minimize the entropy, taking into account the potential uncertainty of the investigations' results. We use simulations to compare our policies to a variety of baseline policies. We find that our policies reduce the MSE of the belief of the security state by up to 50% compared to static baseline policies, and they are robust to high false alert rates and to the investigation errors.

Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack

Abstract: Property inference attacks against machine learning (ML) models aim to infer properties of the training data that are unrelated to the primary task of the model, and have so far been formulated as binary decision problems, i.e., whether or not the training data have a certain property. However, in industrial and healthcare applications, the proportion of labels in the training data is quite often also considered sensitive information. In this paper we introduce a new type of property inference attack that unlike binary decision problems in literature, aim at inferring the class label distribution of the training data from parameters of ML classifier models. We propose a method based on \emph{shadow training} and a \emph{meta-classifier} trained on the parameters of the shadow classifiers augmented with the accuracy of the classifiers on auxiliary data. We evaluate the proposed approach for ML classifiers with fully connected neural network architectures. We find that the proposed \emph{meta-classifier} attack provides a maximum relative improvement of $52\%$ over state of the art.