遭受网络攻击的分布式系统中证据的可信融合
Credible fusion of evidence in distributed system subject to cyberattacks
AI总结：
针对分布式系统遭网络攻击时的证据可信融合问题，提出基于条件可信度、加密状态分解重构及攻击者识别校正的一致性算法，并在无人侦察集群仿真中验证其有效性。
摘要：
鉴于分布式系统面临窃听和网络攻击等对抗行为，如何确保证据融合结果可信成为一个必须解决的课题。不同于假设节点相互协作的传统研究，本文关注证据融合的三项要求，即保护证据隐私、识别攻击者并排除其证据，以及消除由随机噪声和干扰导致的证据间高冲突。为此，本文提出一种抵御网络攻击的可信证据融合算法。首先，基于条件化可信度构建融合策略，以避免高冲突造成的反直觉融合结果。在该策略下，分布式证据融合被转化为基于多源证据条件可信度的加权平均值（WAVCCME）的平均一致性问题，这意味着相比现有算法，其一致性过程更简洁、计算复杂度更低。其次，设计了一种带权重加密的状态分解与重构策略，并保证其在有向图下隐私保护的有效性：针对不同邻居将状态分解为不同的随机子状态，以防御内部窃听者；在重构中对子状态权重进行加密，以防范系统外窃听者。最后，通过邻居间广播和节点状态比较来识别攻击者的身份和类型，并采用所提出的带状态校正的更新规则实现 WAVCCME 的一致性。正常节点的状态被证明收敛至其 WAVCCME，而攻击者的证据被排除在融合之外，这一点在分布式无人侦察集群上的仿真中得到了验证。
英文摘要：
Given that distributed systems face adversarial behaviors such as eavesdropping and cyberattacks, how to ensure the evidence fusion result is credible becomes a must-be-addressed topic. Different from traditional research that assumes nodes are cooperative, we focus on three requirements for evidence fusion, i.e., preserving evidence's privacy, identifying attackers and excluding their evidence, and dissipating high-conflicting among evidence caused by random noise and interference. To this end, this paper proposes an algorithm for credible evidence fusion against cyberattacks. Firstly, the fusion strategy is constructed based on conditionalized credibility to avoid counterintuitive fusion results caused by high-conflicting. Under this strategy, distributed evidence fusion is transformed into the average consensus problem for the weighted average value by conditional credibility of multi-source evidence (WAVCCME), which implies a more concise consensus process and lower computational complexity than existing algorithms. Secondly, a state decomposition and reconstruction strategy with weight encryption is designed, and its effectiveness for privacy-preserving under directed graphs is guaranteed: decomposing states into different random sub-states for different neighbors to defend against internal eavesdroppers, and encrypting the sub-states' weight in the reconstruction to guard against out-of-system eavesdroppers. Finally, the identities and types of attackers are identified by inter-neighbor broadcasting and comparison of nodes' states, and the proposed update rule with state corrections is used to achieve the consensus of the WAVCCME. The states of normal nodes are shown to converge to their WAVCCME, while the attacker's evidence is excluded from the fusion, as verified by the simulation on a distributed unmanned reconnaissance swarm.
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