Gnutella对等网中抵御DDoS攻击的评估算法

为了有效降低恶意节点利用泛洪查找机制对网络造成的破坏,提高对等网抵御DDoS攻击的自适应力,提出了基于马尔科夫的评估(ME)算法。运用可信和信誉机制对节点的历史行为进行评估,确保节点所获取的信息来源节点的可信;通过节点邻居信息的交互将恶意节点尽早识别、隔离,并将恶意消息的传播控制在局部范围,增强抵御DDoS攻击的效能。仿真实验结果表明,该算法能有效地隔离恶意节点,阻止恶意消息的传输,增强Gnutella对等网对基于泛洪DDoS攻击的容忍度。

An Evaluation Algorithm Improving Resilience Against DDoS Attack in Gnutella Peer-to-Peer

In unstructured Peer-to-Peer (P2P) systems such as Gnutella, the general routing search mechanism used is to blindly flood a query to the network among peers. However, the blindly flooding search mechanism makes the whole network subjected to distributed denial of service (DDoS) attacks. In order to alleviate or minimize the bad effect due to behavior of malicious nodes making use of the flooding search mechanism, we propose the Markov-based evaluation (ME) mechanism in which reputation is applied as incentive pattern called a trusted based incentive scheme. Trust based incentive is enabled by evaluating the transaction history of the peer and changing the peer's significance or capacity within the P2P network based on this evaluation. Our simulation study shows that this approach can effectively isolate the malicious peer and its message transmitting and improve resilience against DDoS attack.