Emergency supplies allocation is a critical task in post-disaster response, as ineffective or delayed decisions can directly lead to increased human suffering and loss of life. In practice, emergency managers must make rapid allocation decisions over multiple periods under incomplete information and highly unpredictable demand, making robust and adaptive decision support essential. However, existing allocation approaches face several challenges: (1) Those traditional approaches rely heavily on predefined uncertainty sets or probabilistic models, and are inherently static, making them unsuitable for multi-period, dynamically allocation problems; and (2) while reinforcement learning (RL) technique is inherently suitable for dynamic decision-making, most existing RL-base approaches assume fixed demand, making them unable to cope with the non-stationary demand patterns seen in real disasters. To address these challenges, we first establish a multi-period and multi-objective emergency supplies allocation problem with demand uncertainty and then formulate it as a two-player zero-sum Markov game (TZMG). Demand uncertainty is modeled through an adversary rather than predefined uncertainty sets. We then propose RESA, a novel RL framework that uses adversarial training to learn robust allocation policies. In addition, RESA introduces a combinatorial action representation and reward clipping methods to handle high-dimensional allocations and nonlinear objectives. Building on RESA, we develop RESA_PPO by employing proximal policy optimization as its policy optimizer. Experiment results with realistic post-disaster data show that RESA_PPO achieves near-optimal performance, with an average gap of only 3.7% in terms of the objective value of the formulated problem, from the theoretical optimum derived by exact solvers. Moreover, RESA_PPO outperforms all baseline methods, including heuristic and standard RL methods, by at least 5.25% on average.

Federated continual learning (FCL) must preserve useful historical knowledge while learning from evolving and statistically heterogeneous client streams. However, indiscriminate replay can retain client-specific bias, noise, or task conflicts and thereby cause negative transfer. In this paper, we propose Adaptive Multi-Factor Accurate Forgetting for Heterogeneous Federated Continual Learning (AMAF-FCL), a selective memory-management framework that jointly assesses replay reliability and adapts the influence of generated historical features. AMAF-FCL achieves this goal by (1) modeling historical knowledge in feature space with a conditional real-valued non-volume-preserving (RealNVP) normalizing flow; (2) combining class-conditional likelihood, predictive uncertainty, and global distribution consistency; and (3) adjusting replay weights according to client–global heterogeneity. The local objective combines a classification loss for learning the current task, a reliability-weighted replay loss for retaining useful historical knowledge, and a feature-distillation loss for limiting drift in the feature representation. In the EMNIST long-task-pool (EMNIST-LTP) benchmark, each client learns six two-class tasks drawn from a client-specific set of handwritten letters. AMAF-FCL achieves 51.2% average accuracy and 7.6% average forgetting, improving over the likelihood-based AF-FCL baseline by 3.7 and 1.5 percentage points, respectively. In the cross-domain digit-and-fashion setting, it reports 71.2% average accuracy and 6.8% average forgetting; with four noisy clients, it obtains 56.1% average accuracy and 9.8% average forgetting. These results indicate that multi-factor reliability assessment and heterogeneity-aware adaptive forgetting improve the balance between useful knowledge retention and harmful-knowledge suppression in heterogeneous FCL.

Trusted-Execution-Environment (TEE)-based Confidential Virtual Machines (CVMs) deployed in public clouds currently lack verifiable trust proof of their running state, undermining remote users' confidence in using them. While Virtual Trusted Platform Module (vTPM) techniques can be leveraged to generate such proof for CVMs, existing vTPM-based approaches suffer from three key weaknesses: lack of a well-defined root-of-trust, lack of vTPM protection, and lack of vTPM's trust proof. Consequently, it is hard for these approaches to generate verifiable trust proof to represent the states of remote users' CVMs. This paper proposes T3CVM, an approach that leverages vTPM to generate verifiable trust proof for remote users' AMD SEVbased CVMs. T3CVM comprises three components and a trusted boot scheme to overcome the weaknesses of the existing state-ofthe- art approaches and then increase user's confidence in the trust proof. Our approach allows trusted computing-based applications to run seamlessly in the trusted CVM. Additionally, our approach can be extended to the CVMs based on other VM-based TEE technologies, such as Intel TDX. We formally analyze T3CVM's security and evaluate its performance through a prototype implementation.

In cloud storage, cryptographic techniques are commonly used to support secure search and matching while protecting users' data privacy. Public-key encryption with equality test (PKEET), a widely studied primitive, allows an authorized computation to determine whether two ciphertexts contain the same message without decrypting either of them. Applying PKEET to outsourced sensitive data nevertheless creates three practical risks. (1) Broad authorization may let a requester compare records beyond the approved case; (2) an untrusted cloud may skip the computation or report a false answer; (3) a cloud holding reusable testing material may silently compare stored records without permission and learn hidden relationships among them. Existing approaches do not jointly address these risks. We therefore propose AVPKEET with exact-request authorization and public verification. Exact-request authorization restricts each approval to one stored-ciphertext/query pair and one registered requester, preventing the approval from being transferred to another context. An untrusted cloud worker stores and forwards encrypted data, while a small isolated controller validates the exact request and performs the test without releasing its long-term secret. Every response includes a publicly checkable proof, so anyone holding the request and response can verify the reported “same” or “different” answer without a verification secret. We establish correctness and formal security within the stated threat model. Experiments show that every complete protocol algorithm finishes within 9.1 ms at the 95th percentile on one CPU thread and that a complete request–response transcript occupies 1,809 bytes, demonstrating the practicality of the proposed scheme.

Trusted Execution Environments (TEEs) such as ARM TrustZone are widely deployed in IoT and embedded devices to build trust anchors and protect sensitive code and data. The TEE is often assumed to be trusted despite lacking strong runtime evidence. However, recent attacks targeting TEE kernels and applications have underscored the unreliability of this assumption, thereby breaking the chain of full-lifecycle trust in TrustZone-based devices. In this paper, we aim to address the missing piece of the trust of TrustZone-based devices, namely TEE runtime integrity measurement and its extension mechanisms. We first classify TEE threats into three attack surfaces. Based on this threat model, we propose Runtime Integrity Measurement Architecture for ARM TrustZone-based TEEs (TZRIMA), comprising two subsystems within the TEE: Secure Measurement Agent (SMA) and Remote Attestation Agent (RAA). The SMA subsystem is responsible for conducting policy-driven integrity measurement, appraisal, and secure logging of the TEE-specific components, including the TEE kernel, pTAs, shared libraries, user-TAs, and TEE internal core calls. The RAA subsystem aims to export evidence for trusted state verification. As a concrete realization of verifier-side evidence appraisal, TZRIMA adopts a Trusted Third Party (TTP)-assisted challenge–response remote attestation workflow, where the TTP acts as a trusted source of certificates and reference information. We implement TZRIMA on OP-TEE for Raspberry Pi 3B+ and evaluate its security and performance, showing that it provides effective TEE runtime integrity measurement with acceptable runtime latency and limited verification overhead in the TTP-assisted remote attestation workflow.

Consensus mechanisms are essential to distributed networked systems, ensuring consistent state agreement under unreliable communication and node failures. Among such mechanisms, Practical Byzantine Fault Tolerance (PBFT) has been widely adopted in practice and extensively extended through numerous variants to address scalability in large-scale and heterogeneous networks. However, they still lack 1) adaptability to dynamic changes in network topology or fault distribution, and/or 2) capability of mitigating the gap between the consensus system behavior quantification and the consensus optimization approaches. These degrade consensus robustness in terms of consensus probability and consensus response time. To address these challenges, we first propose a hierarchical node architecture for PBFT. We then develop a scalable Semi-Markov Process (SMP)-based analytical model that captures the stochastic, time-dependent evolution of consensus states across multiple layers. Numerical and simulation results confirm the model's accuracy and show that the hierarchical structure sustains reliable operation up to 30% faulty nodes while maintaining low latency. Building on this modeling foundation, we propose a reinforcement learning-based optimization approach that formulates hierarchical grouping as a Markov decision process and solves it using proximal policy optimization. Experimental evaluation demonstrates that our approach improves consensus probability and reduces latency compared with static grouping baselines.

Public Blockchain-based covert transmission (CT) can address the limitations of traditional CT methods. Monero is a blockchain-based cryptocurrency with strong privacy protection techniques. However, existing Monero-based CT methods are limited to unicast scenarios. If applied directly to group CT scenarios, they would lead to a significant increase in transaction volume as the number of receivers increases. Meanwhile, existing Bitcoin/Ethereum-based group CT methods at least face three challenges, including susceptibility to key and identity inference attacks, information leakage during off-chain negotiations, and exposure of communication channels. This paper proposes a Monero-Based Group CT approach (MBGCT), which enables on-chain group key (used for receivers to filter covert transactions and extract messages) issuance, fine-grained access control of messages, and covert transaction identification and decryption isolation. MBGCT can ensure confidentiality of group keys, unforgeability of messages, integrity of each transmitted message, obscurity of covert channels, isolation of key generation from key management, and enhanced anonymity. As a result, MBGCT can not only prevent information leakage and channel exposure, but also resist the attacks of entity impersonation, data tampering, key and identity inference. We implemented MBGCT in Monero client v0.18.1.0, and validated its capability of high embedding rates, low transaction fees, and high execution efficiency on the Monero public chain Stagenet.

The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning framework that combines DRL with large language model (LLM) reasoning to enable safe, compliant, and economically viable path planning. Experimental results demonstrate that our method significantly outperforms existing baselines across multiple metrics, including data collection rate, collision avoidance, successful landing, regulatory compliance, and energy efficiency. These results validate the effectiveness of our approach in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.

Advanced wireless communication and artificial intelligence technologies are significantly facilitating the deployment of driverless taxis (DTs). To ensure high-quality of DT services, data sharing among DTs is essential during service provision. However, at least three key challenges are then raised in terms of security and privacy: 1) guaranteeing data authenticity with traceability; 2) enabling flexible data authorization and preserving privacy amid complex data ownership; and 3) protecting DT privacy under frequent data sharing. Most existing works address only one aspect, such as data authenticity, privacy preservation, or single-entity authorization. To address these challenges, we propose a novel Multi-authorized, Privacy-protected, and Traceable Data Sharing (MPTDS) scheme for DTs. Firstly, MPTDS upholds data privacy preservation by designing the data sharing credential for each DT and the data access credential for each service provider. Data sharing credential also ensures anonymity and unlinkability of DTs. Secondly, MPTDS ensures data integrity and origin authentication by designing the credential-based authentication technique for DT, which also enables the traceability. Additionally, MPTDS facilitates the data multi-authorization by designing the data sharing credential multi-party issuance technique without sacrificing the non-frameabiltiy. We conduct security analyses and performance evaluations to validate the security properties and practicality of MPTDS.

To facilitate widespread deployment of FPGA-SoC (Field Programmable Gate Array System-on-Chip) TEE (Trusted Execution Environments) in public clouds, this study proposes TRCTEE, a Trusted Runtime-Customizable TEE approach. TRCTEE incorporates a user-controllable Trusted Platform Module (TPM) which is decoupled from FPGA-SoC and implemented in secure and trusted node, providing both a security-enhanced TEE and a TPM 2.0-supported method for runtime customization for FPGA-SoCs. The principal contributions encompass: (i) Propose a novel FPGA-SoC-CtrlTPM architecture delivering full TPM 2.0 operations for FPGA-SoC TEE; (ii) Enhance security of FPGA-SoC TEE by exploring the trusted boot scheme, TEE-dedicated TPM service, and dynamically measure, deploy, and invoke users’ IPs (Intellectual Properties); (iii) Extend standard TPM command set to support FPGA-SoC-specific security operations. We present a functional prototype implemented on the Xilinx Zynq UltraScale+ MPSoC platform, accompanied by security analysis and performance benchmarking. Experimental results demonstrate TRCTEE’s practical feasibility and enhanced security properties.

Confidential Virtual Machines (CVMs), such as AMD SEV-SNP, enable cloud tenants to run security-sensitive workloads, but tenants can rely on the execution of these workloads only when they can trust the CVM. This trust requires continuous integrity assurance from CVM launch to the current runtime state, including initial trust establishment at launch and subsequent runtime integrity assurance. Existing works help establish launch-time trust and protect parts of runtime integrity, but they do not fully address the integrity of file-backed user-space executable objects, such as main executables, program interpreters, and dynamically loaded shared objects, that may be loaded or mapped dynamically during execution inside CVMs. In this paper, we propose Privilege-Separated User-space Integrity Enforcement (PS-UIE), an approach for enforcing the integrity of user-space executable objects inside AMD SEV-SNP-based CVMs. PS-UIE consists of a privilege-separated architecture and three mechanisms. The architecture separates the authority for integrity measurement and enforcement from the measured targets by placing it in a higher-privileged protected domain. Built on this architecture, PS-UIE provides policy lifecycle management, execution-time integrity enforcement, and evidence export and verification mechanisms. It enables policy-controlled integrity measurement and enforcement for user-space executable objects and generates verifiable runtime evidence. We implement PS-UIE on an AMD SEV-SNP platform. The security analysis and performance evaluation show that PS-UIE enforces the integrity of user-space executable objects on the covered execute-permission grant paths and provides verifiable runtime evidence while incurring acceptable overhead.

Reinforcement learning (RL) has shown considerable potential in autonomous driving (AD), yet its vulnerability to perturbations remains a critical barrier to real-world deployment. As a primary countermeasure, adversarial training improves policy robustness by training the AD agent in the presence of an adversary that deliberately introduces perturbations. Existing approaches typically model the interaction as a zero-sum game with continuous attacks. However, such designs overlook the inherent asymmetry between the agent and the adversary and then fail to reflect the sparsity of safety-critical risks, rendering the achieved robustness inadequate for practical AD scenarios. To address these limitations, we introduce criticality-aware robust RL (CARRL), a novel adversarial training approach for handling sparse, safety-critical risks in autonomous driving. CARRL consists of two interacting components: a risk exposure adversary (REA) and a risk-targeted robust agent (RTRA). We model the interaction between the REA and RTRA as a general-sum game, allowing the REA to focus on exposing safety-critical failures (e.g., collisions) while the RTRA learns to balance safety with driving efficiency. The REA employs a decoupled optimization mechanism to better identify and exploit sparse safety-critical moments under a constrained budget. However, such focused attacks inevitably result in a scarcity of adversarial data. The RTRA copes with this scarcity by jointly leveraging benign and adversarial experiences via a dual replay buffer and enforces policy consistency under perturbations to stabilize behavior. Experimental results demonstrate that our approach reduces the collision rate by at least 22.66% across all cases compared to state-of-the-art baseline methods.

Metaverse as-a-Service (MaaS) enables Metaverse tenants to execute their APPlications (MetaAPP) by allocating Metaverse resources in the form of Metaverse service functions (MSF). Usually, each MSF is deployed in a virtual machine (VM) for better resiliency and security. However, these MSFs along with VMs and virtual machine monitors (VMM) running them may encounter software aging after prolonged continuous operation. Then, there is a decrease in MetaAPP dependability, namely, the dependability of the MSF chain (MSFC), consisting of MSFs allocated to MetaAPP. This paper aims to investigate the impact of both software aging and rejuvenation techniques on MetaAPP dependability in the scenarios, where both active components (MSF, VM and VMM) and their backup components are subject to software aging. We develop a hierarchical model to capture behaviors of aging, failure, and recovery by applying Semi-Markov process and reliability block diagram. Numerical analysis and simulation experiments are conducted to evaluate the approximation accuracy of the proposed model and dependability metrics. We then identify the key parameters for improving the MetaAPP/MSFC dependability through sensitivity analysis. The investigation is also made about the influence of various parameters on MetaAPP/MSFC dependability.

Unmanned Aerial Vehicle (UAV) cluster services are crucial for promoting the low-altitude economy by enabling scalable, flexible, and adaptive aerial networks. To meet diverse service demands, clusters must dynamically incorporate a New UAVs (NUAVs) or an Existing UAV (EUAV). However, achieving sustained service reliability remains challenging due to the need for efficient and scalable NUAV authentication, privacy-preserving cross-cluster authentication for EUAVs, and robust protection of the cluster session key, including both forward and backward secrecy. To address these challenges, we propose a Lightweight and Privacy-Preserving Cluster Authentication and Session Key Update (LP2-CASKU) scheme tailored for dynamic UAV clusters in low-altitude economy networks. LP2-CASKU integrates an efficient batch authentication mechanism that simultaneously authenticates multiple NUAVs with minimal communication overhead. It further introduces a lightweight cross-cluster authentication mechanism that ensures EUAV anonymity and unlinkability. Additionally, a secure session key update mechanism is incorporated to maintain key confidentiality over time, thereby preserving both forward and backward secrecy. We provide a comprehensive security analysis and evaluate LP2-CASKU performance through both theoretical analysis and OMNeT++ simulations. Experimental results demonstrate that, compared to the baseline, LP2-CASKU achieves a latency reduction of 82.8

The evolution of 6G networks from bit-oriented transmission to semantic communication renders communication systems vulnerable to emerging security threats targeting information interpretation, specifically semantic distortion. Consequently, identifying critical attack paths is essential for understanding and mitigating semantic attack propagation at the network level. Reinforcement learning (RL) has been increasingly adopted to identify critical paths in complex networks, yet existing solutions typically rely on agents tailored to specific environments characterized by discrete nodes and fixed semantic attributes. These discrete formulations constrain scalability in large-scale networks and hinder generalization across dynamic topologies. To alleviate these limitations, we propose Sem-DRL (Semantic-aware Deep Reinforcement Learning), a continuous RL framework featuring invariant observation and action spaces. Sem-DRL leverages Graph Neural Networks (GNNs) to extract permutation-invariant embeddings, which enables zero-shot generalization across unseen network topologies. By decoupling the action space from the network size, the proposed framework ensures scalability in large-scale networks. Furthermore, Sem-DRL utilizes PLMs as semantic judges to quantify distortion rewards within a continuous latent space. Extensive experiments demonstrate that the proposed framework achieves stable convergence in networks with up to 500 nodes and attains zero-shot transfer success rates of 76%–90% across previously unseen network topologies.

Trusted Execution Environments (TEEs) such as ARM TrustZone are widely used in IoT and embedded devices to protect sensitive code and data. However, most existing defenses focus on secure boot or REE-side monitoring and provide little visibility into the runtime integrity of the TEE. This leaves TrustZone-based devices exposed to persistent TEE compromises. We propose Policy-Driven Runtime Integrity Measurement and Attestation (PDRIMA), a runtime integrity protection approach for TrustZone-based TEEs. PDRIMA systematically analyzes TEE attack surfaces and introduces two in-TEE subsystems: a Secure Monitor Agent (SMA) that performs policy-driven measurement, appraisal, logging, and time-based re-measurement over the TEE kernel, static components, user-TAs, and security-critical system calls; and a Remote Attestation Agent (RAA) that aggregates tamper-evident evidence and exposes a remote attestation protocol for verifying. We analyze PDRIMA's security against identified attack surfaces, implement a prototype on OP-TEE for Raspberry Pi 3B+, and evaluate its performance overhead to indicate its practicability.

Attack-path planning plays a key role in proactive cybersecurity because of its ability in helping defenders anticipate adversaries and uncover critical vulnerabilities. This paper proposes GAPPO, a novel deep reinforcement learning-based attack path planning scheme that integrates Graph Attention Networks (GAT) and expert knowledge into Proximal Policy Optimization (PPO). There are three mechanisms in GAPPO. The first is using GAT to produce graph-structure-aware embeddings that emphasize critical connections, enabling expressive state representations for decision making. The second is a ruled-based action masking mechanism, which incorporates expert knowledge to prune the action space based on node dependencies and then to prevent illegal actions from negatively impacting training. The third is combining the results of the first two mechanisms into PPO for attack path planning. Our extensive experimental results demonstrate that GAPPO outperforms existing methods in terms of faster convergence and higher-quality attack paths across diverse scenarios.

Container technology, as the key enabler behind microservice architectures, is widely applied in Cloud and Edge Computing. A long and continuous running of operating system (OS) host-ing container-based services can encounter software aging that leads to performance deterioration and even causes system fail-ures. OS rejuvenation techniques can mitigate the impact of software aging but the rejuvenation trigger interval needs to be carefully determined to reduce the downtime cost due to rejuve-nation. This paper proposes a comprehensive semi-Markov-based approach to quantitatively evaluate the effect of OS reju-venation on the dependability and the performance of a con-tainer-based service. In contrast to the existing studies, we nei-ther restrict the distributions of time intervals of events to be exponential nor assume that backup resources are always avail-able. Through the numerical study, we show the optimal con-tainer-migration trigger intervals that can maximize the de-pendability or minimize the performance of a container-based service.

Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data from the global model, introduces new privacy concerns. Specifically, the gradient exchanges during the unlearning process can leak sensitive information about deleted data. In this paper, we introduce DRAGD, a novel attack that exploits gradient discrepancies before and after unlearning to reconstruct forgotten data. We also present DRAGDP, an enhanced version of DRAGD that leverages publicly available prior data to improve reconstruction accuracy, particularly for complex datasets like facial images. Extensive experiments across multiple datasets demonstrate that DRAGD and DRAGDP significantly outperform existing methods in data reconstruction.Our work highlights a critical privacy vulnerability in federated unlearning and offers a practical solution, advancing the security of federated unlearning systems in real-world applications.

Deep reinforcement learning (DRL) has emerged as a promising paradigm for autonomous driving. However, despite their advanced capabilities, DRL-based policies remain highly vulnerable to adversarial attacks, posing serious safety risks in real-world deployments. Investigating such attacks is crucial for revealing policy vulnerabilities and guiding the development of more robust autonomous systems. While prior attack methods have made notable progress, they still face several challenges: 1) they often rely on high-frequency attacks, yet critical attack opportunities are typically context-dependent and temporally sparse, resulting in inefficient attack patterns; 2) restricting attack frequency can improve efficiency but often results in unstable training due to the adversary's limited exploration. To address these challenges, we propose an adaptive expert-guided adversarial attack method that enhances both the stability and efficiency of attack policy training. Our method first derives an expert policy from successful attack demonstrations using imitation learning, strengthened by an ensemble Mixture-of-Experts architecture for robust generalization across scenarios. This expert policy then guides a DRL-based adversary through a KL-divergence regularization term. Due to the diversity of scenarios, expert policies may be imperfect. To address this, we further introduce a performance-aware annealing strategy that gradually reduces reliance on the expert as the adversary improves. Extensive experiments demonstrate that our method achieves outperforms existing approaches in terms of collision rate, attack efficiency, and training stability, especially in cases where the expert policy is sub-optimal.