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.

In this paper, a novel privacy-preserving signature scheme for network coding is proposed, which can mitigate both intra-generation and inter-generation pollution attacks efficiently, and leverage the inherent security property of random network coding to prevent eavesdropping attacks at the same time. Our scheme is constructed on a bi-linear map between two multiplicative cyclic groups with the same order, and its security relies on the hardness of the discrete logarithm problem. We proved that the construction of our scheme is correct, and also showed that our scheme is resistant against both pollution attacks and eavesdropping attacks by demonstrating that the probabilities of an adversary recovering the native messages and forging a valid signature for a bogus packet are both negligible functions of the security parameter. We evaluated the performance of our scheme in terms of communication overhead and computational complexity, and carried out extensive experiments to compare the running time of different procedures between our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated.

In this paper, a novel Mobile-Coverage scheme is proposed to improve the coverage and lifetime of hybrid sensor networks, and thus the target region can be completely covered with minimum number of mobile sensor nodes and the energy consumption can be reduced in hybrid sensor networks at the same time. Our scheme is applied to a hybrid network comprising of both static and mobile sensor nodes, which are randomly deployed in a rectangle area. We derived the minimum number of sensor nodes as well as the mobile nodes required to cover the target region completely. Compared to the existing works, the minimum number of mobile sensor nodes required in this paper is significantly smaller, which implies that the movement distance and energy consumption of mobile nodes in our scheme is reduced. Moreover, we proved that the construction of our scheme based on the Virtual Force and Voronoi Graph (VFVG) is correct, and then derived the optimal trade-off between the coverage of the target region and energy consumption of mobile nodes. Our scheme can achieve the maximum coverage which is 98.7% by minimum number of mobile sensor nodes. Meanwhile, a connectivity rate of 76.84% can also be achieved by our connectivity evaluation method. Finally, a new topology construction algorithm-Topology Construction and Maintenance based on Trust (TCMT) is created to further increase the lifetime of the sensor network. We also evaluated the performance of our scheme in terms of coverage and lifetime, and carried out extensive experiments to compare the coverage and energy consumption in our scheme and several previous schemes. The experimental results showed that our scheme is more efficient than the previous schemes simulated.

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.

Due to characteristics, such as the openness and exposure of intersatellite 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: 1) state trust and 2) 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 multipath 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.

This paper investigates the intrinsic relationship between secure network coding and secure cloud storage. We propose a novel homomorphic signature scheme and then illustrate a method to construct the network coding based secure cloud storage (NCSCS) protocol, wherein the security of the homomorphic signature scheme is the foundation of the NCSCS protocol. By employing network coding and homomorphic signature technique, the proposed NCSCS system can serve as both cloud service provider and router of a communication network at the same time, where the public auditing of the outsourced data is enabled. Finally, we perform the simulation of the proposed protocol and evaluate its performance. The security and efficiency can be validated from the simulation results.