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Found 80 papers, 5 papers with code
Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories
This chapter starts with a systemic view toward cyber risks and presents the confluence of game theory, control theory, and learning theories, which are three major pillars for the design of cyber resilience mechanisms to counteract increasingly sophisticated and evolving threats in our networks and organizations.
Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare
This chapter concludes with a discussion of the challenges associated with FMs and their application in the domain of cybersecurity.
Disentangling Resilience from Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms
The article concludes by discussing the interplay between robustness and resilience, suggesting that a comprehensive theory of resilience and quantification metrics, and formalization through game-theoretic frameworks are necessary.
Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games
To address these limitations, we propose conjectural online learning (\textsc{col}), an online method for generic \textsc{aisg}s. \textsc{col} uses a forecaster-actor-critic (\textsc{fac}) architecture where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures.
Automated Security Response through Online Learning with Adaptive Conjectures
We study automated security response for an IT infrastructure and formulate the interaction between an attacker and a defender as a partially observed, non-stationary game.
Integrated Cyber-Physical Resiliency for Power Grids under IoT-Enabled Dynamic Botnet Attacks
The attacker aims to exploit this vulnerability to enable a successful physical compromise, while the system operator's goal is to ensure a normal operation of the grid by mitigating cyber risks.
Self-Confirming Transformer for Locally Consistent Online Adaptation in Multi-Agent Reinforcement Learning
In the multi-agent RL (MARL) setting, this distribution shift may arise from the nonstationary opponents (exogenous agents beyond control) in the online testing who display distinct behaviors from those recorded in the offline dataset.
Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments
In the area of learning-driven artificial intelligence advancement, the integration of machine learning (ML) into self-driving (SD) technology stands as an impressive engineering feat.
A First Order Meta Stackelberg Method for Robust Federated Learning
Previous research has shown that federated learning (FL) systems are exposed to an array of security risks.
Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning
To equip the navigation agent with sample-efficient learning and {zero-shot} generalization, this work proposes a novel physics-informed RL (PIRL) where a distance-to-target-based cost (standard in e2e) is augmented with physics-informed reward shaping.
AI Liability Insurance With an Example in AI-Powered E-diagnosis System
As an economic solution to compensate for potential damages, AI liability insurance is a promising market to enhance the integration of AI into daily life.
Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study
\textit{Intelligent Navigation Systems} (INS) are exposed to an increasing number of informational attack vectors, which often intercept through the communication channels between the INS and the transportation network during the data collecting process.
Detection in Human-sensor Systems under Quantum Prospect Theory using Bayesian Persuasion Frameworks
Human-sensor systems have a wide range of applications in fields such as robotics, healthcare, and finance.
Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach
The increasing connectivity and intricate remote access environment have made traditional perimeter-based network defense vulnerable.
Cluster Forming of Multiagent Systems in Rolling Horizon Games with Non-uniform Horizons
Consensus and cluster forming of multiagent systems in the face of jamming attacks along with reactive recovery actions by a defender are discussed.
QoS Based Contract Design for Profit Maximization in IoT-Enabled Data Markets
The contract design creates a pricing structure for on-demand sensing data for IoT users.
A Rolling Horizon Game Considering Network Effect in Cluster Forming for Dynamic Resilient Multiagent Systems
A two-player game-theoretic problem on resilient graphs in a multiagent consensus setting is formulated.
Commitment with Signaling under Double-sided Information Asymmetry
This work considers a double-sided information asymmetry in a Bayesian Stackelberg game, where the leader's realized action, sampled from the mixed strategy commitment, is hidden from the follower.
Cognitive Level-$k$ Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving
To ensure traffic safety in self-driving environments and respond to vehicle-human interaction challenges such as jaywalking, we propose Level-$k$ Meta Reinforcement Learning (LK-MRL) algorithm.
Two-Player Incomplete Games of Resilient Multiagent Systems
Evolution of agents' dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents.
Stackelberg Meta-Learning Based Control for Guided Cooperative LQG Systems
To this end, we develop a meta-learning-based Stackelberg game-theoretic framework to address the challenges in the guided cooperative control for linear systems.
Quantum Man-in-the-middle Attacks: a Game-theoretic Approach with Applications to Radars
The detection and discrimination of quantum states serve a crucial role in quantum signal processing, a discipline that studies methods and techniques to process signals that obey the quantum mechanics frameworks.
On the Resilience of Traffic Networks under Non-Equilibrium Learning
We investigate the resilience of learning-based \textit{Intelligent Navigation Systems} (INS) to informational flow attacks, which exploit the vulnerabilities of IT infrastructure and manipulate traffic condition data.
Multi-Agent Learning for Resilient Distributed Control Systems
In this book chapter, we present a multi-agent system (MAS) framework for distributed large-scale control systems and discuss the role of MAS learning in resiliency.
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis
It leads to two online attack schemes: Intermittent Attack and Persistent Attack, which enable the attacker to learn an optimal sampling attack, defined by an $\epsilon$-first-order stationary point, within $\mathcal{O}(\epsilon^{-2})$ iterations.
ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance Policies
Incentive design is a proactive and non-invasive approach to achieving compliance by aligning an insider's incentive with the defender's security objective, which motivates (rather than commands) an insider to act in the organization's interests.
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation
The attacker can also gradually trick the ADP learner into learning the same `nefarious' policy by consistently feeding the learner a falsified cost signal that stays close to the actual cost signal.
Autonomous and Resilient Control for Optimal LEO Satellite Constellation Coverage Against Space Threats
As on-orbit repairs are challenging, a distributed and autonomous protection mechanism is necessary to ensure the adaptation and self-healing of the satellite constellation coverage from different attacks.
Bayesian Promised Persuasion: Dynamic Forward-Looking Multiagent Delegation with Informational Burning
A revelation-principle-like design regime is established to show that the persuasion with belief hierarchies can be fully characterized by correlating the randomization of the agents' local BPD mechanisms with the persuasion as a direct recommendation of the future promises.
RADAMS: Resilient and Adaptive Alert and Attention Management Strategy against Informational Denial-of-Service (IDoS) Attacks
In this work, we identify and formally define a new type of proactive attentional attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints.
Combating Informational Denial-of-Service (IDoS) Attacks: Modeling and Mitigation of Attentional Human Vulnerability
The numerical results illustrate how AM strategies can alleviate the severity level and the risk of IDoS attacks.
Reinforcement Learning for Feedback-Enabled Cyber Resilience
In this work, we review the literature on RL for cyber resilience and discuss cyber resilience against three major types of vulnerabilities, i. e., posture-related, information-related, and human-related vulnerabilities.
ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention
The results show that the visual aids can statistically increase the attention level and improve the accuracy of phishing recognition from 74. 6% to a minimum of 86%.
Game-Theoretic Frameworks for Epidemic Spreading and Human Decision Making: A Review
In this review, we motivate the game-theoretic approach to human decision-making amid epidemics.
The Confluence of Networks, Games and Learning
In addition to existing research works on game-theoretic learning over networks, we highlight several new angles and research endeavors on learning in games that are related to recent developments in artificial intelligence.
Informational Design of Dynamic Multi-Agent System
We propose a direct information design approach that incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors.
Transactive Resilience in Renewable Microgrids: A Contract-Theoretic Approach
Renewable energy-based microgrids play a critical role in future smart grids.
Assets Defending Differential Games with Partial Information and Selected Observations
Moreover, we show that the observation choices of the defender and the attacker can be decoupled and the Nash observation strategies can be found by solving two independent optimization problems.
Self-Triggered Markov Decision Processes
We study the co-design problems of the control policy and the triggering policy to optimize two pre-specified cost criteria.
On the Equilibrium Elicitation of Markov Games Through Information Design
An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors.
A Pursuit-Evasion Differential Game with Strategic Information Acquisition
We also show that when the game's horizon goes to infinity, the Nash observation strategy is to observe periodically, and the expected distance between the pursuer and the evader goes to zero with a bounded second moment.
Feedback Capacity of Parallel ACGN Channels and Kalman Filter: Power Allocation with Feedback
In this paper, we relate the feedback capacity of parallel additive colored Gaussian noise (ACGN) channels to a variant of the Kalman filter.
Blackwell Online Learning for Markov Decision Processes
This work provides a novel interpretation of Markov Decision Processes (MDP) from the online optimization viewpoint.
Fundamental Limits of Controlled Stochastic Dynamical Systems: An Information-Theoretic Approach
We first consider the scenario where the plant (i. e., the dynamical system to be controlled) is linear time-invariant, and it is seen in general that the lower bounds are characterized by the unstable poles (or nonminimum-phase zeros) of the plant as well as the conditional entropy of the disturbance.
Independent Elliptical Distributions Minimize Their $\mathcal{W}_2$ Wasserstein Distance from Independent Elliptical Distributions with the Same Density Generator
This short note is on a property of the $\mathcal{W}_2$ Wasserstein distance which indicates that independent elliptical distributions minimize their $\mathcal{W}_2$ Wasserstein distance from given independent elliptical distributions with the same density generators.
The Spectral-Domain $\mathcal{W}_2$ Wasserstein Distance for Elliptical Processes and the Spectral-Domain Gelbrich Bound
In this short note, we introduce the spectral-domain $\mathcal{W}_2$ Wasserstein distance for elliptical stochastic processes in terms of their power spectra.
Cross-Layer Coordinated Attacks on Cyber-Physical Systems: A LQG Game Framework with Controlled Observations
On the other hand, the interactions between the attacker and the defender in the physical layer significantly impact the observation and jamming strategies.
Fundamental Stealthiness-Distortion Tradeoffs in Dynamical Systems under Injection Attacks: A Power Spectral Analysis
In this paper, we analyze the fundamental stealthiness-distortion tradeoffs of linear Gaussian dynamical systems under data injection attacks using a power spectral analysis, whereas the Kullback-Leibler (KL) divergence is employed as the stealthiness measure.
Optimal Curing Strategy for Competing Epidemics Spreading over Complex Networks
The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network.
Locally-Aware Constrained Games on Networks
We use \textit{awareness levels} to capture the scope of the network constraints that players are aware of.
Independent Gaussian Distributions Minimize the Kullback-Leibler (KL) Divergence from Independent Gaussian Distributions
This short note is on a property of the Kullback-Leibler (KL) divergence which indicates that independent Gaussian distributions minimize the KL divergence from given independent Gaussian distributions.
Fundamental Limits of Obfuscation for Linear Gaussian Dynamical Systems: An Information-Theoretic Approach
In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach.
Deceptive Kernel Function on Observations of Discrete POMDP
This paper studies the deception applied on agent in a partially observable Markov decision process.
Channel Leakage, Information-Theoretic Limitations of Obfuscation, and Optimal Privacy Mask Design for Streaming Data
In this paper, we first introduce the notion of channel leakage as the minimum mutual information between the channel input and channel output.
Modeling and Assessment of IoT Supply Chain Security Risks: The Role of Structural and Parametric Uncertainties
Supply chain security threats pose new challenges to security risk modeling techniques for complex ICT systems such as the IoT.
Cryptography and Security Systems and Control Systems and Control
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM
Distributed machine learning algorithms play a significant role in processing massive data sets over large networks.
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals
Focusing on adversarial manipulation on the cost signals, we analyze the performance degradation of TD($\lambda$) and $Q$-learning algorithms under the manipulation.
Fundamental Limits of Prediction, Generalization, and Recursion: An Entropic-Innovations Perspective
We also investigate the implications of the results in analyzing the fundamental limits of generalization in fitting (learning) problems from the perspective of prediction with side information, as well as the fundamental limits of recursive algorithms by viewing them as generalized prediction problems.
Feedback Capacity and a Variant of the Kalman Filter with ARMA Gaussian Noises: Explicit Bounds and Feedback Coding Design
In this paper, we relate a feedback channel with any finite-order autoregressive moving-average (ARMA) Gaussian noises to a variant of the Kalman filter.
Control Challenges for Resilient Control Systems
In this chapter, we introduce methods to address resiliency issues for control systems.
Information-Theoretic Performance Limitations of Feedback Control: Underlying Entropic Laws and Generic $\mathcal{L}_{p}$ Bounds
In this paper, we utilize information theory to study the fundamental performance limitations of generic feedback systems, where both the controller and the plant may be any causal functions/mappings while the disturbance can be with any distributions.
Relativistic Control: Feedback Control of Relativistic Dynamics
As such, the feedback linearization together with the linear controller compose the overall relativistic feedback control law.
Fundamental Limitations in Sequential Prediction and Recursive Algorithms: $\mathcal{L}_{p}$ Bounds via an Entropic Analysis
In this paper, we obtain fundamental $\mathcal{L}_{p}$ bounds in sequential prediction and recursive algorithms via an entropic analysis.
RIoTS: Risk Analysis of IoT Supply Chain Threats
Securing the supply chain of information and communications technology (ICT) has recently emerged as a critical concern for national security and integrity.
Cryptography and Security
Generic Bounds on the Maximum Deviations in Sequential Prediction: An Information-Theoretic Analysis
In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach.
On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning
In this paper, we propose a generic framework for devising an adaptive approximation scheme for value function approximation in reinforcement learning, which introduces multiscale approximation.
Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense
The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the \texttt{`3A'} defense paradigm.
Cryptography and Security
A Dynamic Game Framework for Rational and Persistent Robot Deception With an Application to Deceptive Pursuit-Evasion
The deception is rational as robots aim to achieve their deception goals at minimum cost.
Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes
In this work, we apply infinite-horizon Semi-Markov Decision Process (SMDP) to characterize a stochastic transition and sojourn time of attackers in the honeynet and quantify the reward-risk trade-off.
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals
This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL.
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective
In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach.
ADMM-based Networked Stochastic Variational Inference
SVI poses variational inference as a stochastic optimization problem and solves it iteratively using noisy gradient estimates.
A Game-Theoretic Approach to Design Secure and Resilient Distributed Support Vector Machines
Distributed Support Vector Machines (DSVM) have been developed to solve large-scale classification problems in networked systems with a large number of sensors and control units.
Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries
The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed learning algorithms.
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning
We show that the risks of the target tasks in the nodes without the data of the source tasks can also be reduced using the information transferred from the nodes who contain the data of the source tasks.
A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization
Data ecosystems are becoming larger and more complex due to online tracking, wearable computing, and the Internet of Things.
Cryptography and Security
A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics.
A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
First, a machine learner declares a privacy protection level, and then users respond by choosing their own perturbation amounts.
Dynamic Privacy For Distributed Machine Learning Over Network
Privacy-preserving distributed machine learning becomes increasingly important due to the recent rapid growth of data.
Approximate dynamic programming using fluid and diffusion approximations with applications to power management
Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations.
