The Internet of unmanned agents (IUAs) has emerged as a transformative technology, driven by advancements in unmanned devices and 5G communications, leading to significant progress in autonomous systems and distributed networks. Despite these advancements, IUA faces significant challenges in data security, privacy preservation, and distributed learning, particularly in grading sensitive data and efficient distributed grade model training across diverse unmanned devices within IUA context. To address these issues, this article proposes a novel scheme, FedHDL-IUA, a privacy-enhanced distributed data grading scheme designed specifically for the IUA context. This scheme leverages a federated learning (FL) framework, ensuring the privacy of local sensitive data while optimizing the performance of distributed data grading models. By combining bidirectional long short-term memory (BiLSTM) and residual networks (ResNets), the scheme can effectively capture feature dependencies in diverse network traffic data, thereby enhancing the accuracy and efficiency of the data grading model. The simulation experiments are conducted using two open-source datasets and a private dataset, and the results show that FedHDL-IUA can efficiently and effectively grade the traffic data in both centralized and FL modes and outperforms other existing schemes and traditional deep learning models in terms of performance.

The Internet of Vehicles (IoV) is an emerging technology that enhances traffic security and transportation efficiency by enabling smart, connected vehicles to communicate and exchange messages. IoV networks are a key component of intelligent transportation systems in smart cities. However, these networks are vulnerable to malicious vehicles that disseminate deceptive messages or impersonate legitimate entities, which compromises network security. These adversarial vehicles jeopardize the integrity and availability of the IoV network, exposing it to various security threats, including both insider and outsider attacks. Such attacks can severely undermine the trust and reliability of communication between legitimate vehicles. To address these challenges, we propose TMSU-IoV, a UAV-assisted trust management scheme that integrates identity authentication technique and trust evaluation mechanism to ensure secure communication of connected vehicles in IoV networks. To counteract outsider attacks, we introduce a certificateless signature-based authentication method that guarantees the authenticity of messages exchanged between vehicles and UAVs. To mitigate insider threats, we propose a quality of service (QoS)-based trust evaluation mechanism. This mechanism consists of a prior trust evaluation method and a posterior trust evaluation method, designed to enhance both the credibility and timeliness of trust evaluation for connected vehicles. Formal security analysis confirms that the TMSU-IoV effectively resists a variety of insider and outsider attacks. Performance evaluation experiments demonstrate that the TMSU-IoV can accurately assess the trust levels of connected vehicles and outperform traditional trust evaluation methods.

The importance of social network alignment (SNA) for various downstream applications, such as social network information fusion and e-commerce recommendation, has prompted numerous professionals to develop and share SNA tools. However, malicious actors can exploit these tools to integrate sensitive user information, thereby posing cybersecurity risks. Although many researchers have explored attacking SNA (ASNA) through network modification attacks to protect users, practical feasibility remains challenging. In this study, we propose an effective node injection attack via a dynamic programming framework (DPNIA) to address the problem of modeling and solving ASNA within a limited time and balancing the costs and benefits. DPNIA models ASNA as a problem of maximizing the number of confirmed incorrect correspondent node pairs with greater similarity scores than the pairs between existing nodes, thereby making ASNA solvable. A cross-network evaluation method is employed directly to identify node vulnerabilities, facilitating progressive attacking from easy to difficult. In addition, an optimal injection strategy searching method based on dynamic programming is used to determine which links should be added between the injected and existing nodes, thereby enhancing the effectiveness of the attack at a low cost. Experiments on four real-world datasets demonstrated that DPNIA consistently and significantly surpasses various baselines when attacking both multiple networks simultaneously and a single network.

The adoption of lightweight container technology enables the cross-architecture deployment of Tiny Machine Learning (TinyML) models, while the implementation of container escape detectors ensures the security of both models and applications. However, a significant challenge faced by TinyML-based detectors is model aging, which leads to a substantial decline in their effectiveness as attack patterns evolve. Most existing approaches address this issue by retraining models through the labeling of new samples. However, this process can be costly and challenging to implement for updating models in resource-constrained UAVs networks. In this paper, we begin by analyzing the correlation of threat data and observe that throughout evolution, different versions of container escape attacks tend to maintain semantically identical or similar system calls. This observation prompts us to approach the model aging problem from a novel perspective: if the model can acquire knowledge of these fundamental system calls, it will be capable of effectively detecting emerging new attacks. Based on this perspective, we have developed sysE to capture system call data that remains unchanged or exhibits similarities to container escape attacks during evolution. This augmentation complements six TinyML-based detectors. Experimental results obtained from a large-scale evolving dataset demonstrate that our proposed approach effectively mitigates the aging rate of these models, reducing it from 7.3% to 21.5%. Additionally, it significantly decreases the labeling effort required from 28.06% to 65.47%.