arXiv subjects

Terrence J. Moore

Publications and source records attributed to Terrence J. Moore.

18 recordsLinked to original sources

arXiv2026

Covert Multi-Hop Communications for Heterogeneous Networks With Multiple Wardens

This paper investigates covert multi-hop communications in heterogeneous wireless networks monitored by multiple passive wardens. To maximize network-wide covertness while satisfying a strict end-to-end rate requirement, we jointly optimize routing, modality selection, and transmit power. Under a simultaneous multi-hop transmission scheme, we analyze the detection capabilities of two distinct warden models: colluding wardens employing a central fusion center, and non-colluding wardens operating independently. For both models, we derive optimal detectors and exact expressions for the detection error probability (DEP). In addition, to reduce the complexity of evaluating the DEP, we develop highly accurate closed-form approximations based on gamma moment matching and establish rigorous DEP lower bounds using Kullback-Leibler (KL) divergence. Building on this theoretical foundation, we propose an efficient two-stage optimization algorithm that decouples link-level resource allocation from network-level path selection. By translating the KL divergence bounds into a novel, low-complexity routing metric, which universally simplifies to a linear summation of signal-to-noise ratios, we substantially reduce the computational overhead compared to conventional per-hop detection-based metrics. Finally, numerical simulations validate the theoretical analysis and demonstrate the near-optimal performance of the proposed framework.

eess.SP↗

arXiv2026

Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning

When algorithmic decisions depend on data distributed across institutions, how can we ensure that an individual's outcome does not change arbitrarily based on a protected attribute? We study this question in vertical federated learning (VFL), where features are split across parties, sensitive attributes may be private, and proxies for protected characteristics can be scattered across institutional boundaries under strict privacy constraints. Our focus is on individual-level counterfactual stability, i.e., per-instance prediction consistency under protected-attribute interventions as formalized in the causal fairness literature, rather than group parity guarantees such as demographic parity or equalized odds. We propose SCC-VFL, a server-centric framework for enforcing selective counterfactual consistency (SCC) at the individual level in VFL. SCC-VFL operationalizes a given policy specification by combining three components: (i) differentially private, graph-free discovery of feature roles into non-descendants, policy-permitted mediators, and impermissible proxies using only a formally private sketch of the sensitive attribute, with a formal per-release privacy that does not extend to the full training pipeline; (ii) masked counterfactual generation that edits only mediators while fixing non-descendants and suppressing proxy leakage; and (iii) server-side enforcement via an SCC consistency loss that penalizes impermissible prediction changes under protected-attribute interventions. Across three real-world datasets spanning credit, healthcare, and criminal justice, SCC-VFL maintains or improves predictive accuracy while sharply reducing decision flip rates by up to 98% relative to strong baselines. It also lowers attribute-inference attack success and improves robustness, demonstrating favorable utility-fairness-privacy trade-offs in realistic VFL deployments.

cs.CY↗

arXiv2026

RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility

Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model training without centralized data aggregation. However, FL frameworks that protect privacy often sacrifice fairness and reliability. Differential privacy can reduce data leakage, but it may also obscure sensitive attributes needed for bias correction, thereby worsening performance gaps across demographic groups. This work studies the privacy-fairness trade-off in FL-based object detection and introduces RESFL, an integrated framework that jointly improves both objectives. RESFL combines adversarial privacy disentanglement with uncertainty-guided fairness-aware aggregation. The adversarial component uses a gradient reversal layer to suppress sensitive attribute information, reducing privacy risks while preserving fairness-relevant structure. The uncertainty-aware aggregation component uses an evidential neural network to adaptively weight client updates, prioritizing contributions with lower fairness disparities and higher confidence. This produces robust and equitable FL model updates. Experiments in high-stakes autonomous vehicle settings show that RESFL achieves high mAP on FACET and CARLA, reduces membership-inference attack success by 37%, reduces the equality-of-opportunity gap by 17% relative to the FedAvg baseline, and maintains stronger adversarial robustness. Although evaluated in autonomous driving, RESFL is domain-agnostic and can be applied to a broad range of application domains beyond this setting.

cs.LG↗

arXiv2026

DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving

End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vision-language-guided reinforcement learning (RL) methods introduce semantic feedback, they often rely on static prompts and fixed objectives, limiting adaptability to dynamic driving scenes. We present DriveMind, a unified semantic reward framework that integrates: (i) a contrastive Vision-Language Model (VLM) encoder for stepwise semantic anchoring; (ii) a novelty-triggered VLM encoder-decoder, fine-tuned via chain-of-thought (CoT) distillation, for dynamic prompt generation upon semantic drift; (iii) a hierarchical safety module enforcing kinematic constraints (e.g., speed, lane centering, stability); and (iv) a compact predictive world model to reward alignment with anticipated ideal states. DriveMind achieves 19.4 +/- 2.3 km/h average speed, 0.98 +/- 0.03 route completion, and near-zero collisions in CARLA Town 2, outperforming baselines by over 4% in success rate. Its semantic reward generalizes zero-shot to real dash-cam data with minimal distributional shift, demonstrating robust cross-domain alignment and potential for real-world deployment.

cs.RO↗

arXiv2026

Simultaneous Multi-Modal Covert Communications: Analysis and Optimization

This paper investigates the problem of covert communications in a heterogeneous wireless network where multiple communication modalities are used simultaneously. In this setup, a legitimate transmitter sends confidential data to its receiver by selecting multiple modalities with the goal of maximizing communication covertness against a passive adversary (Willie) while satisfying a transmission rate requirement. We analyze two distinct scenarios for a given observation time by Willie. The two scenarios are: (i) Willie knows the modalities selected by the friendly transmitter, and (ii) Willie is unaware of the selected modalities. We first derive the optimal detector for Willie that minimizes the detection error probability (DEP) in both cases. For the first scenario, we derive an exact expression for the DEP and provide a computationally efficient approximation. For the second scenario, we introduce the DEP expressions in the low-signal-to-noise ratio (SNR) regime at Willie. Building on this analysis, we propose a novel low-complexity modality set selection technique designed to maximize the DEP subject to a rate constraint. Numerical simulations validate the derived analytical expressions and demonstrate that the proposed modality set selection technique achieves near-optimal performance, outperforming benchmark schemes.

eess.SP↗

arXiv2026

Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles

Autonomous vehicles must remain safe and effective when encountering rare long-tailed scenarios or cyber-physical intrusions during driving. We present RAIL, a risk-aware human-in-the-loop framework that turns heterogeneous runtime signals into calibrated control adaptations and focused learning. RAIL fuses three cues (curvature actuation integrity, time-to-collision proximity, and observation-shift consistency) into an Intrusion Risk Score (IRS) via a weighted Noisy-OR. When IRS exceeds a threshold, actions are blended with a cue-specific shield using a learned authority, while human override remains available; when risk is low, the nominal policy executes. A contextual bandit arbitrates among shields based on the cue vector, improving mitigation choices online. RAIL couples Soft Actor-Critic (SAC) with risk-prioritized replay and dual rewards so that takeovers and near misses steer learning while nominal behavior remains covered. On MetaDrive, RAIL achieves a Test Return (TR) of 360.65, a Test Success Rate (TSR) of 0.85, a Test Safety Violation (TSV) of 0.75, and a Disturbance Rate (DR) of 0.0027, while logging only 29.07 training safety violations, outperforming RL, safe RL, offline/imitation learning, and prior HITL baselines. Under Controller Area Network (CAN) injection and LiDAR spoofing attacks, it improves Success Rate (SR) to 0.68 and 0.80, lowers the Disengagement Rate under Attack (DRA) to 0.37 and 0.03, and reduces the Attack Success Rate (ASR) to 0.34 and 0.11. In CARLA, RAIL attains a TR of 1609.70 and TSR of 0.41 with only 8000 steps.

cs.AI↗

arXiv2025

DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System

We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared Mental Models (SMM). Designed for mission-critical applications such as surveillance and rescue, DASH introduces "bait tasks" to detect insider threats, e.g., compromised Unmanned Ground Vehicles (UGVs), AI agents, or human analysts, before they degrade team performance. Upon detection, tailored recovery mechanisms are activated, including UGV system reinstallation, AI model retraining, or human analyst replacement. In contrast to existing SMM approaches that neglect insider risks, DASH improves both coordination and security. Empirical evaluations across four schemes (DASH, SMM-only, no-SMM, and baseline) show that DASH sustains approximately 80% mission success under high attack rates, eight times higher than the baseline. This work contributes a practical human-AI teaming framework grounded in shared mental models, a deception-based strategy for insider threat detection, and empirical evidence of enhanced robustness under adversarial conditions. DASH establishes a foundation for secure, adaptive human-machine teaming in contested environments.

cs.HC↗

arXiv2025

Deep Reinforcement Learning for Multi-flow Routing in Heterogeneous Wireless Networks

Due to the rapid growth of heterogeneous wireless networks (HWNs), where devices with diverse communication technologies coexist, there is increasing demand for efficient and adaptive multi-hop routing with multiple data flows. Traditional routing methods, designed for homogeneous environments, fail to address the complexity introduced by links consisting of multiple technologies, frequency-dependent fading, and dynamic topology changes. In this paper, we propose a deep reinforcement learning (DRL)-based routing framework using deep Q-networks (DQN) to establish routes between multiple source-destination pairs in HWNs by enabling each node to jointly select a communication technology, a subband, and a next hop relay that maximizes the rate of the route. Our approach incorporates channel and interference-aware neighbor selection approaches to improve decision-making beyond conventional distance-based heuristics. We further evaluate the robustness and generalizability of the proposed method under varying network dynamics, including node mobility, changes in node density, and the number of data flows. Simulation results demonstrate that our DRL-based routing framework significantly enhances scalability, adaptability, and end-to-end throughput in complex HWN scenarios.

eess.SP↗

arXiv2025

Deep Reinforcement Learning Based Routing for Heterogeneous Multi-Hop Wireless Networks

Routing in multi-hop wireless networks is a complex problem, especially in heterogeneous networks where multiple wireless communication technologies coexist. Reinforcement learning (RL) methods, such as Q-learning, have been introduced for decentralized routing by allowing nodes to make decisions based on local observations. However, Q-learning suffers from scalability issues and poor generalization due to the difficulty in managing the Q-table in large or dynamic network topologies, especially in heterogeneous networks (HetNets) with diverse channel characteristics. Thus, in this paper, we propose a novel deep Q-network (DQN)-based routing framework for heterogeneous multi-hop wireless networks to maximize the end-to-end rate of the route by improving scalability and adaptability, where each node uses a deep neural network (DNN) to estimate the Q-values and jointly select the next-hop relay and a communication technology for transmission. To achieve better performance with the DNN, selecting which nodes to exchange information is critical, as it not only defines the state and action spaces but also determines the input to the DNN. To this end, we propose neighbor node selection strategies based on channel gain and rate between nodes rather than a simple distance-based approach for an improved set of states and actions for DQN-based routing. During training, the model experiences diverse network topologies to ensure generalization and robustness, and simulation results show that the proposed neighbor node selection outperforms simple distance-based selection. Further, we observe that the DQN-based approach outperforms various benchmark schemes and performs comparably to the optimal approach.

eess.SP↗

arXiv2025

Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI

Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges. In this paper, we present the first unified large-scale empirical study of privacy-fairness-utility trade-offs in FL, advancing toward responsible AI deployment. Specifically, we systematically compare Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMC) with fairness-aware optimizers including q-FedAvg, q-MAML, Ditto, evaluating their performance under IID and non-IID scenarios using benchmark (MNIST, Fashion-MNIST) and real-world datasets (Alzheimer's MRI, credit-card fraud detection). Our analysis reveals HE and SMC significantly outperform DP in achieving equitable outcomes under data skew, although at higher computational costs. Remarkably, we uncover unexpected interactions: DP mechanisms can negatively impact fairness, and fairness-aware optimizers can inadvertently reduce privacy effectiveness. We conclude with practical guidelines for designing robust FL systems that deliver equitable, privacy-preserving, and accurate outcomes.

cs.LG↗

arXiv2025

Advancing Human-Machine Teaming: Concepts, Challenges, and Applications

Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems.

cs.HC↗

arXiv2024

Decentralized Covert Routing in Heterogeneous Networks Using Reinforcement Learning

This letter investigates covert routing communications in a heterogeneous network where a source transmits confidential data to a destination with the aid of relaying nodes where each transmitter judiciously chooses one modality among multiple communication modalities. We develop a novel reinforcement learning-based covert routing algorithm that finds a route from the source to the destination where each node identifies its next hop and modality only based on the local feedback information received from its neighboring nodes. We show based on numerical simulations that the proposed covert routing strategy has only negligible performance loss compared to the optimal centralized routing scheme.

cs.NI↗

arXiv2020

A Survey on Centrality Metrics and Their Implications in Network Resilience

Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed for decades, a limited set of centrality metrics have been commonly in use. This paper aims to introduce various existing centrality metrics and discuss their applicabilities and performance based on the results obtained from extensive simulation experiments to encourage their use in solving various computing and engineering problems in networks.

cs.SI↗

arXiv2020

Vulnerability-Aware Resilient Networks: Software Diversity-based Network Adaptation

By leveraging the principle of software polyculture to ensure security in a network, we proposed a vulnerability-based software diversity metric to determine how a network topology can be adapted to minimize security vulnerability while maintaining maximum network connectivity. Our proposed software diversity-based adaptation (SDA) scheme estimates a node's software diversity based on the vulnerabilities of software packages installed on other nodes on attack paths reachable to the node and employs it for edge adaptations, such as removing an edge with a neighboring node that exposes high security vulnerability because two connected nodes use the same software packages or a neighboring node may have high software vulnerability or adding an edge with another node with less or no security vulnerability because the two nodes use different software packages or have low vulnerabilities associated with them. To validate the proposed SDA scheme, we conducted extensive experiments comparing the proposed SDA scheme with counterpart baseline schemes in real networks. Our simulation experimental results proved the outperformance of our proposed SDA compared to the existing counterparts and provided insightful findings in terms of the effectiveness and efficiency of the proposed SDA scheme under three real network topologies with vastly different network density.

cs.CR↗

arXiv2020

Diversity-By-Design for Dependable and Secure Cyber-Physical Systems: A Survey

Diversity-based security approaches have been studied for several decades since the 1970's. The concept of diversity-by-design emerged in the 1980's and, since then, diversity-based system design research has been explored to build more secure and dependable systems. In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, insights, and future work directions for those who want to conduct research on developing secure and dependable cyber-physical systems (CPSs) using diversity as a system design feature. To be specific, this survey paper provides: (i) The common concept of diversity based on a multidisciplinary study of diversity from nine different fields along with the historical evolution of diversity-by-design for security; (ii) The design principles of diversity-based approaches; (iii) The key benefits and caveats of using diversity-by-design; (iv) The key concerns of CPS environments in introducing diversity-by-design; (v) A variety of existing diversity-based approaches based on five different classifications; (vi) The types of attacks mitigated by existing diversity-based approaches; (vii) The overall trends of evaluation methodologies used in diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps identified from this extensive survey.

cs.CR↗

arXiv2019

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Reactive defense mechanisms, such as intrusion detection systems, have made significant efforts to secure a system or network for the last several decades. However, the nature of reactive security mechanisms has limitations because potential attackers cannot be prevented in advance. We are facing a reality with the proliferation of persistent, advanced, intelligent attacks while defenders are often way behind attackers in taking appropriate actions to thwart potential attackers. The concept of moving target defense (MTD) has emerged as a proactive defense mechanism aiming to prevent attacks. In this work, we conducted a comprehensive, in-depth survey to discuss the following aspects of MTD: key roles, design principles, classifications, common attacks, key methodologies, important algorithms, metrics, evaluation methods, and application domains. We discuss the pros and cons of all aspects of MTD surveyed in this work. Lastly, we highlight insights and lessons learned from this study and suggest future work directions. The aim of this paper is to provide the overall trends of MTD research in terms of critical aspects of defense systems for researchers who seek for developing proactive, adaptive MTD mechanisms.

cs.NI↗

arXiv2018

FRVM: Flexible Random Virtual IP Multiplexing in Software-Defined Networks

Network address shuffling is one of moving target defense (MTD) techniques that can invalidate the address information attackers have collected based on the current network IP configuration. We propose a software-defined networking-based MTD technique called Flexible Random Virtual IP Multiplexing, namely FRVM, which aims to defend against network reconnaissance and scanning attacks. FRVM enables a host machine to have multiple, random, time-varying virtual IP addresses, which are multiplexed to a real IP address of the host. Multiplexing or de-multiplexing event dynamically remaps all the virtual network addresses of the hosts. Therefore, at the end of a multiplexing event, FRVM aims to make the attackers lose any knowledge gained through the reconnaissance and to disturb their scanning strategy. In this work, we analyze and evaluate our proposed FRVM in terms of the attack success probability under scanning attacks and target host discovery attacks.

cs.NI↗

arXiv2017

An efficient alternative to Ollivier-Ricci curvature based on the Jaccard metric

We study Ollivier-Ricci curvature, a discrete version of Ricci curvature, which has gained popularity over the past several years and has found applications in diverse fields. However, the Ollivier-Ricci curvature requires an optimal mass transport problem to be solved, which can be computationally expensive for large networks. In view of this, we propose two alternative measures of curvature to Ollivier-Ricci which are motivated by the Jaccard coefficient and are demonstrably less computationally intensive, a cheaper Jaccard (JC) and a more expensive generalized Jaccard (gJC) curvature metric. We show theoretically that the gJC closely matches the Ollivier-Ricci curvature for Erdos-Renyi graphs in the asymptotic regime of large networks. Furthermore, we study the goodness of approximation between the proposed curvature metrics and Ollivier-Ricci curvature for several network models and real networks. Our results suggest that in comparison to an alternative curvature metric for graphs, the Forman-Ricci curvature, the gJC exhibits a reasonably good fit to the Ollivier-Ricci curvature for a wide range of networks, while the JC is shown to be a good proxy only for certain scenarios.

cs.SI↗