arXiv subjects

Christoforos N. Hadjicostis

Publications and source records attributed to Christoforos N. Hadjicostis.

At least 19 recordsLinked to original sources

arXiv2025

Synthesis of State-Attack Strategies for Anonymity and Opacity Violation in Discrete Event Systems

Attacks, including the manipulation of sensor readings and the modification of actuator commands, pose a significant challenge to the security and privacy of automated systems. This paper considers discrete event systems that can be modeled with nondeterministic finite state automata that are susceptible to state attacks. A state attack allows an intruder to learn whether or not the current state of a system falls into certain subsets of states. The intruder has a limited total number of state attacks at its disposal, but can launch state attacks at arbitrary instants of its choosing. We are interested on violations of current-state anonymity (resp. opacity), i.e., situations where the intruder, based on the sequence of observations generated by the system and the outcome of any performed state attacks, can ascertain the exact current state of the system (resp. that the current state of the system definitely resides in a subset of secret states). When the system violates current-state anonymity (resp. opacity) under a bounded number of state attacks, a subsequent question is whether the intruder can design an attack strategy such that anonymity-violating (resp. opacity-violating) situations will always be reached. In this latter case, we also design an attack strategy that guarantees that the system will reach a violating situation regardless of system actions. We provide pertinent complexity analysis of the corresponding verification algorithms and examples to illustrate the proposed methods.

cs.FL↗

arXiv2025

Decentralized State Estimation and Opacity Verification Based on Partially Ordered Observation Sequences

In this paper, we investigate state estimation and opacity verification problems within a decentralized observation architecture. Specifically, we consider a discrete event system whose behavior is recorded by a set of observation sites. These sites transmit the partially ordered sequences of observations that they record to a coordinator whenever a synchronization occurs. To properly analyze the system behavior from the coordinator's viewpoint, we first introduce the notion of a Complete Synchronizing Sequence structure (CSS structure), which concisely captures the state evolution of each system state upon different information provided by the observation sites. Based on the CSS structure, we then construct corresponding current-state and initial-state estimators for offline state estimation at the coordinator. When used to verify state-isolation properties under this decentralized architecture, the use of CSS structure demonstrates a significant reduction in complexity compared with existing approaches in the literature. In particular, we discuss how to verify initial-state opacity at the coordinator, as well as a novel opacity notion, namely current-state-at-synchronization opacity.

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arXiv2025

Distributed Average Consensus in Wireless Multi-Agent Systems with Over-the-Air Aggregation

In this paper, we address the average consensus problem of multi-agent systems over wireless networks. We propose a distributed average consensus algorithm by invoking the concept of over-the-air aggregation, which exploits the signal superposition property of wireless multiple-access channels. The proposed algorithm deploys a modified version of the well-known Ratio Consensus algorithm with an additional normalization step for compensating for the arbitrary channel coefficients. We show that, when the noise level at the receivers is negligible, the algorithm converges asymptotically to the average for time-invariant and time-varying channels. Numerical simulations corroborate the validity of our results.

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arXiv2025

Opacity Enforcement by Edit Functions Under Incomparable Observations

As an information-flow privacy property, opacity characterizes whether a malicious external observer (referred to as an intruder) is able to infer the secret behavior of a system. This paper addresses the problem of opacity enforcement using edit functions in discrete event systems modeled by partially observed deterministic finite automata. A defender uses the edit function as an interface at the output of a system to manipulate actual observations through insertion, substitution, and deletion operations so that the intruder will be prevented from inferring the secret behavior of the system. Unlike existing work which usually assumes that the observation capabilities of the intruder and the defender are identical, we consider a more general setting where they may observe incomparable subsets of events generated by the system.To characterize whether the defender has the ability to enforce opacity of the system under this setting, the notion of \emph{$ic$-enforceability} is introduced. Then, the opacity enforcement problem is transformed to a two-player game, with imperfect information between the system and the defender, which can be used to determine a feasible decision-making strategy for the defender. Within the game scheme, an edit mechanism is constructed to enumerate all feasible edit actions following system behavior. We further show that an $ic$-enforcing edit function (if one exists) can be synthesized from the edit mechanism to enforce opacity.

cs.FL↗

arXiv2024

Global and Local Error-Tolerant Decentralized State Estimation under Partially Ordered Observations

We investigate decentralized state estimation for a discrete event system in a setting where the information received at a coordinator may be corrupted or tampered by a malicious attacker. Specifically, a system is observed by a set of (local) observation sites (OSs) which occasionally send their recorded sequences of observations to the coordinator that is in charge of estimating the system state. The malfunctions and attacks, referred to as errors in this paper, include symbol deletions, insertions and replacements, each of which bears a positive cost. Two types of errors, global errors and local errors, are proposed to describe the impact of errors on decentralized information processing. Global errors occur when all OSs record the same error, while local errors occur when different OSs record different errors. Distinguishing these types of errors is important for a proper design of decentralized information processing (so as to be more resilient and better equipped to handle attacks and failures). For each type of error, we propose two methods to efficiently perform state estimation: one based on appropriately modifying the original system and the other based on inferring the matching behavior of the original system. For each method, we adopt an estimation-by-release methodology to design an algorithm for constructing a corresponding synchronizer for state estimation.

eess.SY↗

arXiv2024

Average Consensus over Directed Networks in Open Multi-Agent Systems with Acknowledgement Feedback

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a time-varying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OPENRC algorithm using column-stochasticity and mass-preservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology.

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arXiv2024

ARQ-based Average Consensus over Directed Network Topologies with Unreliable Communication Links

In this paper, we address the discrete-time average consensus problem in strongly connected directed graphs, where nodes exchange information over unreliable error-prone communication links. We enhance the Robustified Ratio Consensus algorithm by exploiting features of the (Hybrid) Automatic Repeat ReQuest - (H)ARQ protocol used for error control of data transmissions, in order to allow the nodes to reach asymptotic average consensus even when information is exchanged over error-prone directional networks. This strategy, apart from handling time-varying information delays induced by retransmissions of erroneous packets, can also handle packet drops that occur when exceeding a predefined packet retransmission limit. Invoking the (H)ARQ protocol allows nodes to: (a) exploit the incoming error-free acknowledgement feedback to initially acquire or later update their out-degree, (b) know whether a packet has arrived or not, and (c) determine a local upper-bound on the delays imposed by the retransmission limit. By augmenting the network's corresponding weight matrix, we show that nodes utilizing our proposed (H)ARQ Ratio Consensus algorithm can reach asymptotic average consensus over unreliable networks, while improving their convergence speed and maintaining low values in their local buffers compared to the current state-of-the-art.

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arXiv2024

Event Concealment and Concealability Enforcement in Discrete Event Systems Under Partial Observation

Inspired by privacy problems where the behavior of a system should not be revealed to an external curious observer, we investigate event concealment and concealability enforcement in discrete event systems modeled as non-deterministic finite automata under partial observation. Given a subset of secret events in a given system, concealability holds if the occurrence of all secret events remains hidden to a curious observer (an eavesdropper). A secret event is said to be (at least under some executions) unconcealable (inferable) if its occurrence can be indirectly determined with certainty after a finite number of observations. When concealability of a system does not hold (i.e., one or more secret events are unconcealable), we analyze how a defender, placed at the interface of the system with the eavesdropper, can be used to enforce concealability. The defender takes as input each observed event of the system and outputs a carefully modified event sequence (seen by the eavesdropper) using event deletion, insertion, or replacement. The defender is said to be C-enforceable if, following the occurrence of the secret events and regardless of subsequent activity generated by the system, it can always deploy a strategy to manipulate observations and conceal the events perpetually. We discuss systematic procedures to detect the presence of unconcealable secret events and verify C-Enforceability using techniques from state estimation and event diagnosis. We also propose a polynomial complexity construction for obtaining one necessary and one sufficient condition for C-Enforceability.

cs.CR↗

arXiv2024

Invariant Properties of Linear-Iterative Distributed Averaging Algorithms and Application to Error Detection

We consider the problem of average consensus in a distributed system comprising a set of nodes that can exchange information among themselves. We focus on a class of algorithms for solving such a problem whereby each node maintains a state and updates it iteratively as a linear combination of the states maintained by its in-neighbors, i.e., nodes from which it receives information directly. Averaging algorithms within this class can be thought of as discrete-time linear time-varying systems without external driving inputs and whose state matrix is column stochastic. As a result, the algorithms exhibit a global invariance property in that the sum of the state variables remains constant at all times. In this paper, we report on another invariance property for the aforementioned class of averaging algorithms. This property is local to each node and reflects the conservation of certain quantities capturing an aggregate of all the values received by a node from its in-neighbors and all the values sent by said node to its out-neighbors (i.e., nodes to which it sends information directly) throughout the execution of the averaging algorithm. We show how this newly-discovered invariant can be leveraged for detecting errors while executing the averaging algorithm.

cs.MA↗

arXiv2023

Reduced-Complexity Verification for K-Step and Infinite-Step Opacity in Discrete Event Systems

Opacity is a property that captures security concerns in cyber-physical systems and its verification plays a significant role. This paper investigates the verifications of K-step and infinite-step weak and strong opacity for partially observed nondeterministic finite state automata. K-step weak opacity is checked by constructing, for some states in the observer, appropriate state-trees, to propose a necessary and sufficient condition. Based on the relation between K-step weak and infinite-step weak opacity, a condition that determines when a system is not infinite-step weak opaque is presented. Regarding K-step and infinite-step strong opacity, we develop a secret-involved projected automaton, based on which we construct secret-unvisited state trees to derive a necessary and sufficient condition for K-step strong opacity. Furthermore, an algorithm is reported to compute a verifier that can be used to obtain a necessary and sufficient condition for infinite-step strong opacity. It is argued that, in some particular cases, the proposed methods achieve reduced complexity compared with the state of the art.

cs.FL↗

arXiv2023

Trustworthy Distributed Average Consensus based on Locally Assessed Trust Evaluations

This paper proposes a distributed algorithm for average consensus in a multi-agent system under a fixed bidirectional communication topology, in the presence of malicious agents (nodes) that may try to influence the average consensus outcome by manipulating their updates. The proposed algorithm converges asymptotically to the average of the initial values of the non-malicious nodes, which we refer to as the trustworthy average, as long as the underlying topology that describes the information exchange among the non-malicious nodes is connected. We first present a distributed iterative algorithm that assumes that each node receives (at each iteration or periodically) side information about the trustworthiness of the other nodes, and it uses such trust assessments to determine whether or not to incorporate messages received from its neighbors, as well as to make proper adjustments in its calculation depending on whether a previously trustworthy neighbor becomes untrustworthy or vice-versa. We show that, as long as the trust assessments for each non-malicious node eventually reflect correctly the status (malicious or non-malicious) of its neighboring nodes, the algorithm guarantees asymptotic convergence to the trustworthy average. We subsequently discuss how the proposed algorithm can be enhanced with functionality that enables each node to obtain trust assessments about its neighbors by utilizing information that it receives from its two-hop neighbors at infrequent, perhaps randomly chosen, time instants.

cs.MA↗

arXiv2023

Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence

We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share salient features. Specifically, the algorithms operate exclusively with quantized values, which means that the information stored, processed and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. Additionally, it is shown that our proposed algorithms compare favorably to algorithms in the current literature. Quantized communication and asynchronous updates increase the required time to completion, but finite-time operation is maintained.

eess.SY↗

arXiv2022

Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication

We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorithms which rely on quantized operation (i.e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps. Furthermore, during the operation of our algorithms, each node is able to determine in a distributed manner whether convergence has been achieved and thus terminate its operation. To the best of the authors' knowledge these algorithms are the first to find the exact solutions under quantized communication (i.e., there is no error in the final calculation). Additionally, note that our network size calculation algorithm is the first in the literature which calculates the exact size of a network in a finite number of steps without introducing a final error. This error in other algorithms can be either due to quantization or asymptotic convergence. In our case, no error is introduced since the desired result is calculated in the form of a fraction involving a quantized numerator and a quantized denominator. Finally, we demonstrate the operation of our algorithms and their potential advantages.

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arXiv2022

Distributed CPU Scheduling Subject to Nonlinear Constraints

This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic feasibility) are addressed in this paper. For the two proposed distributed solutions, one converges over directed weight-balanced networks and the other one over undirected networks. In particular, we elaborate on uniform quantization and discuss the notion of ε-accurate solution, which gives an estimate of how close we can get to the exact optimizer subject to different quantization levels. This work, further, handles general (possibly non-quadratic) strictly convex objective functions with application to CPU allocation among a cloud of data centers via distributed solutions. The results can be used as a coordination mechanism to optimally balance the tasks and CPU resources among a group of networked servers while addressing quantization or limited server capacity. Index Terms: multi-agent systems, sum-preserving resource allocation, distributed optimization, anytime feasibility

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arXiv2022

Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping

In this paper, we present a distributed algorithm which implements the $k$-means algorithm in a distributed fashion for multi-agent systems with directed communication links. The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group.During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck. Also, in order to preserve available resources, nodes are able to distributively determine whether they can terminate the operation of the proposed algorithm. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents to mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.

eess.SY↗

arXiv2022

Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks

In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm which operates over a dynamically changing network, and allows each node to calculate the optimal allocation of data in a finite number of time steps. We show that our proposed algorithm (i) converges to the optimal solution in finite time with very high probability, and (ii) once the optimal solution is reached, each node is able to cease transmissions without needing knowledge of a global parameter such as the network diameter. Furthermore, our algorithm (i) operates exclusively with quantized values (i.e., each node processes and transmits quantized information), (ii) relies on event-driven updates, and (iii) calculates the optimal solution in the form of a quantized fraction which avoids errors due to quantization. Finally, we demonstrate the operation, performance, and potential advantages of our algorithm over random dynamic networks.

eess.SY↗

arXiv2022

Finite Time Privacy Preserving Quantized Average Consensus with Transmission Stopping

Due to their flexibility, battery powered or energy-harvesting wireless networks are employed in diverse applications. Securing data transmissions between wireless devises is of critical importance in order to avoid privacy-sensitive user data leakage. In this paper, we focus on the scenario where some nodes are curious (but not malicious) and try to identify the initial states of one (or multiple) other nodes, while some nodes aim to preserve the privacy of their initial states from the curious nodes. We present a privacy preserving finite transmission event-triggered quantized average consensus algorithm. Its operation is suitable for battery-powered or energy-harvesting wireless network since it guarantees (i) efficient (quantized) communication, and (ii) transmission ceasing (which allows preservation of available energy). Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy. We conclude with a comparison of our algorithm against other algorithms in the existing literature.

eess.SY↗

arXiv2021

Privacy-Preserving Distributed Average Consensus in Finite Time using Random Gossip

In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal the specific value they contribute to the average calculation. We consider a fully-connected (undirected) network in which each pair of components (nodes) can be randomly selected to perform pairwise standard gossip averaging of their values, and propose an enhancement that can be followed by each node that does not want to reveal its initial value to other (curious) nodes. We assume that curious nodes try to identify the initial values of other nodes but do not interfere in the computation in any other way; however, as a worst-case assumption, curious nodes are allowed to collaborate arbitrarily and are assumed to know the privacy-preserving strategy (but not the actual parameters chosen by the nodes that want to preserve their privacy). We characterize precisely conditions on the information exchange that guarantee privacy-preservation for a specific node. The protocol also provides a criterion that allows the nodes to determine, in a distributed manner (while running the enhanced gossip protocol), when to terminate their operation because approximate average consensus has been reached, i.e., all nodes have obtained values that are within a small distance from the exact average of their initial values.

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