Liu Zhiguo, Rong Junlin, Jiang Yingru, Zhang Luxi
Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel). 2023 Oct 15;23(20):8474. doi: 10.3390/s23208474.
The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D-S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module's training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.
卫星网络安全路由算法中采用的传统信任模型在检测恶意节点时准确性有限，并且在面对未知攻击时缺乏适应性。为应对这一挑战，本文介绍了一种基于深度学习和信任管理的安全卫星网络路由技术。该方法采用分布式信任管理概念，使得本文中的所有卫星节点都配备了信任管理和异常检测模块，用于评估相邻节点的安全性。更详细地说，该技术首先使用D-S证据理论对卫星网络节点的通信行为进行预处理，有效减轻VAE模块训练过程中遇到的干扰因素。在这个预处理步骤之后，经过预先处理的信任向量被输入到VAE模块中。一旦VAE模块的训练完成，卫星网络在收集信任证据时可以通过使用安全模块来评估安全因素。最终，这些安全因素可以与蚁群算法中的信息素成分相结合，以引导蚂蚁找到路径。仿真结果证实，所提出的卫星网络安全路由算法有效地应对了恶意节点对网络内数据传输的影响。与卫星网络安全路由算法的传统信任管理模型相比，该算法在平均端到端延迟、丢包率和吞吐量方面都有提升。
