The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones. As edge intelligence continues to evolve, IoT devices have progressed from simple data collection units to nodes capable of executing complex computational tasks. This evolution necessitates the adoption of distributed learning strategies to effectively handle multimodal data in an IoT environment. Furthermore, the real-time nature of data collection and limited local storage on edge devices in IoT call for an online learning paradigm. To address these challenges, we introduce the concept of Multimodal Online Federated Learning (MMO-FL), a novel framework designed for dynamic and decentralized multimodal learning in IoT environments. Building on this framework, we further account for the inherent instability of edge devices, which frequently results in missing modalities during the learning process. We conduct a comprehensive theoretical analysis under both complete and missing modality scenarios, providing insights into the performance degradation caused by missing modalities. To mitigate the impact of modality missing, we propose the Prototypical Modality Mitigation (PMM) algorithm, which leverages prototype learning to effectively compensate for missing modalities. Experimental results on two multimodal datasets further demonstrate the superior performance of PMM compared to benchmarks.

Quality of Service (QoS) routing is a critical technique for delivering differentiated services under limited network resources to meet the specific requirements of endpoint applications. Despite the development of various routing algorithms and management architectures, achieving fine-grained QoS optimization within existing networks remains challenging due to the lack of endpoint control over routing decisions. This paper introduces PanQoSR, the first application of path-aware networks (PAN) in QoS routing. PanQoSR leverages the inherent capabilities of PAN by offloading path computation and selection to the endpoint, enabling flow-level fine-grained QoS optimization. PanQoSR addresses several key challenges in applying PAN to QoS routing. First, by leveraging existing network technologies and protocols, PanQoSR remains fully compatible with legacy networks without requiring significant modifications. Second, by introducing an ε−Constraint Pathfinding (ε−CP) algorithm for intra-AS path computation and a Nonlinear Cost Pathfinding (NCP) algorithm for inter-AS path computation, PanQoSR achieves both high QoS guarantees and computational efficiency. Experimental results show that PanQoSR reduces QoS violation rates by up to 70.8% compared to baselines, while also decreasing inter-AS path computation time by 22.4% to 65.6%.

Abstract In cloud-edge collaboration scenarios, attackers pose significant security risks by compromising computational nodes and using them to infiltrate other nodes and networks. Ensuring the security of cloud-edge collaboration is crucial for protecting sensitive data, preventing disruptions to critical services, and safeguarding infrastructure in increasingly interconnected and digitized societies. Traditional passive defense mechanisms are often inadequate in dealing with the complex and dynamic nature of modern network threats. In recent years, Moving Target Defense (MTD) has become an important research direction, disrupting adversaries’ reconnaissance and exploitation phases by dynamically shuffling the attack surface. However, existing MTD strategies have some shortcomings, such as single-dimensional movement strategies, poor flexibility and a lack of historical information analysis. To overcome these challenges, we propose a reinforcement learning-based approach for host address and port hopping (RLAPH). First, the approach strengthens system security through coordinated decision-making across IP address and port, leveraging both historical data and current information to make accurate and adaptive decisions. Second, a reward function is carefully designed to balance the trade-off between system overhead and security. Finally, validation experiments conducted in a simulated environment show that the proposed method effectively enhances defense performance while minimizing system overhead, highlighting its robustness and applicability.

Due to characteristics such as the openness and exposure of inter-satellite links, Low-Earth Orbit (LEO) satellite networks are subject to heightened vulnerability to malicious attacks compared to ground-based networks. Given various security risks, implementing trust management in LEO satellite networks becomes imperative. However, existing trust management schemes tailored for this scenario are vulnerable to a spectrum of attacks, resulting in low detection rates and poor network performance. To address the above issues, we propose BiTrust, a hybrid trust management scheme for secure data transmission in LEO satellite networks. BiTrust introduces two distinct types of trust: state trust and behavior trust. State trust leverages remote attestation technology to verify the authenticity of node identities and the integrity of their functions, ensuring that nodes consistently disseminate reliable behavior trust announcements to safeguard the trust plane. Behavior trust, on the other hand, utilizes a trust model to quantify the real-time data forwarding behavior characteristics of nodes, thereby maintaining the reliability of the data plane. To precisely quantify behavior trust, we design a trust model based on multi-path trust propagation. By integrating an unstable penalty term, the proposed trust model can effectively defend against dynamic dropping misbehavior. Besides, to facilitate the deployment of BiTrust in a distributed manner, we introduce a two-hop rely message mechanism and a query-answer mechanism to support the construction of behavior trust. Experimental results confirm the efficacy of BiTrust, demonstrating a significant improvement of up to 64.3% in data transmission rate under highly untrusted environments while maintaining affordable overhead.

Network coding is a promising technique to improve the throughput and robustness of wireless ad hoc networks. However, the packet‐mixing nature of network coding also renders it more prone to pollution attacks. Most existing schemes to combat pollution attacks did not consider the defender's resource limit, nor the trade‐off between defensive performance and other metrics such as delay and resource consumption. The authors investigate how to achieve such a trade‐off optimally by proposing a two‐player strategic game model between the attack and the defender. In this model, the utilities of both players are well defined, and thus the defender can obtain its best strategy by maximising its utility. To do so, a graph‐based simulated annealing algorithm is proposed to derive the utility‐maximising strategy. Finally, they conduct extensive experiments to evaluate their scheme from different aspects. The results show that their scheme can achieve better utility than existing schemes, and is more computationally efficient in the meanwhile. Moreover, their scheme can obtain a sub‐optimal solution within a small number of iterations, which implies that it can be implemented in the short‐session communication scenario where it is required to find a sufficiently good solution within a short time.

Byzantine attack is a severe security concern in network coding enabled wireless ad hoc networks, because the malicious nodes can easily inject bogus packets into the information flow and cause an epidemic propagation of pollution. In this paper, we address the Byzantine attack by proposing a malicious node identification scheme, which can achieve a high identification accuracy on malicious nodes and protect the benign nodes from being mis-identified as attackers. We consider two practical challenges, namely, 1) only a fraction of the intermediate nodes can be deployed as defenders; and 2) the malicious nodes are intelligent-they pretend to be legitimate nodes probabilistically to reduce the chances of being identified. Theoretical analysis and extensive simulations show that our scheme performs well even under the conditions mentioned above. Furthermore, we conduct a series of comparisons between our scheme and several existing schemes, which show that our scheme outperforms them in both identification accuracy and valid throughput during the identification procedure. Finally, we present a two-player game theory framework to find the optimal strategy for the defender, and also provide a case study of the defender's strategy optimization.