基于蚁群算法的加强型可抵御攻击信任管理模型

通过将网络节点推荐行为分析和网络恶意节点密度的自适应机制纳入信誉度评价过程,提出了基于蚁群算法的加强型可抵御攻击信任管理模型--EAraTRM,以解决传统信任模型因较少考虑节点的推荐欺骗行为而导致容易在恶意节点的合谋攻击影响下失准的问题。在对比研究中发现,EAraTRM可以在网络中恶意节点密度达到90%,其他传统信任模型已经失效的情况下,仍保持较高的正确性。实验结果表明,EAraTRM能提高节点评价其他节点信誉度时的精度,并降低整个网络中恶意节点间进行合谋攻击的成功率。

Enhanced attack-resistible ant-based trust and reputation model

Traditional trust and reputation models do not pay enough attention to nodes'deceit in recommendation, so their reputation evaluation may be affected by malicious nodes' collusion. A trust and reputation model named Enhanced Attack Resistible Ant-based Trust and Reputation Model (EAraTRM) was proposed, which is based on ant colony algorithm. Node recommendation behaviors analysis and adaptive mechanism to malicious nodes density were added into reputation evaluation of EAraTRM to overcome the shortage of traditional models. Simulation experiments show that EAraTRM can restrain the collusion of malicious nodes, and give more accurate reputation evaluation results, even when 90% nodes in a network are malicious and the comparison models have failed.