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An effective node-removal method against P2P botnets

机译:针对P2P僵尸网络的有效节点去除方法

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Nowadays, there is no effective solution to take down P2P botnets. In this paper, we have proposed a probabilistic node-removal method for P2P botnets forming random network, which reduces the resilience of the C&C channel of P2P botnets more effectively compared to other previous methods. We have introduced a new approach for selecting the critical nodes. In our method, based on collecting the receiving times of commands in some bots, the command entry points are identified, and then the critical nodes are determined. Our approach can be used for cases in which the botmaster inject the commands into the botnet from more than one point. If the topology of botnets is unknown, our method can be applied to the reconstructed topology. In this paper, a new metric is defined for accurately measuring the power of the botnet after removing the critical nodes. This metric is compatible with several entry points of the commands. We have validated our method through simulations. The obtained results indicate that we can effectively isolate significantly more nodes from the origin compared to the other node-removal methods. In the best results, we have managed to isolate the first node of a botnet with 500 peers by removing only 8% of the bots. In the same botnet, 22% of the bots are isolated by only 75 removals, while the best other methods isolate 1% of the bots by 466 removals. Moreover, in two cases of P2P botnet with multiple command entry points, our method has isolated around 14 and 22 times more nodes compare to the best method among the others.
机译:如今,没有有效的解决方案来取下P2P僵尸网络。在本文中,我们提出了一种用于形成随机网络的P2P僵尸网络的概率节点去除方法,其与其他先前的方法相比,将P2P僵尸网络的C&C信道的抵抗降低。我们介绍了一种用于选择关键节点的新方法。在我们的方法中,基于在一些机器人中收集命令的接收时间,识别命令入口点,然后确定临界节点。我们的方法可用于Botmaster将命令从一个以上从一个点注入僵尸网络的情况。如果僵尸网络的拓扑未知,我们的方法可以应用于重建的拓扑。在本文中,定义了一种新的度量,用于在移除临界节点之后精确测量僵尸网络的电源。此度量标准与命令的若干入口点兼容。我们通过模拟验证了我们的方法。所获得的结果表明,与其他节点去除方法相比,我们可以从原点有效地隔离更多的节点。在最佳结果中,我们已经设法通过删除仅8%的机器人将僵尸网络的第一个节点与500个对等体隔离。在相同的僵尸网络中,22%的机器人仅被75移除隔离,而最佳的其他方法将1%的机器人分离为466次移除。此外,在具有多个命令入口点的两个P2P僵尸网络的情况下,我们的方法距离与其他的最佳方法相比的节点左右约14和22倍。

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