基于时间复杂网络的移动恶意软件遏制研究
Exploiting Temporal Complex Network Metrics in Mobile Malware Containment
摘要：
恶意手机蠕虫通过短距离蓝牙联系在设备之间传播，类似于人类和其他生物病毒的传播。最近的工作采用流行病学和复杂网络的模型来分析恶意软件的传播和修补特定节点的影响。这些方法采用了移动网络的静态观点，即，通过聚集随时间出现的所有边，这导致真实交互的近似表示：相反，这些网络本质上是动态的，并且边的出现和消失受到人类接触的排序的高度影响，这是现有复杂网络测量根本无法捕获的。在本文中，我们首先研究如何通过免疫关键节点（即使通过静态或时间介数中心性度量仔细选择）阻止恶意软件传播是无效的：这是由于这些网络中的替代路径的丰富性。然后，我们引入了一个时间感知的遏制策略，传播补丁消息从节点具有高的时间接近中心，并使用三个真实世界的数据集显示其有效性。时间上的接近度允许识别能够快速到达大多数节点的节点：我们表明，该方案可以减少蜂窝网络资源消耗和相关成本，同时在有限的时间内完全遏制恶意软件。
英文摘要：
Malicious mobile phone worms spread between devices via short-range Bluetooth contacts, similar to the propagation of human and other biological viruses. Recent work has employed models from epidemiology and complex networks to analyse the spread of malware and the effect of patching specific nodes. These approaches have adopted a static view of the mobile networks, i.e., by aggregating all the edges that appear over time, which leads to an approximate representation of the real interactions: instead, these networks are inherently dynamic and the edge appearance and disappearance is highly influenced by the ordering of the human contacts, something which is not captured at all by existing complex network measures. In this paper we first study how the blocking of malware propagation through immunisation of key nodes (even if carefully chosen through static or temporal betweenness centrality metrics) is ineffective: this is due to the richness of alternative paths in these networks. Then we introduce a time-aware containment strategy that spreads a patch message starting from nodes with high temporal closeness centrality and show its effectiveness using three real-world datasets. Temporal closeness allows the identification of nodes able to reach most nodes quickly: we show that this scheme can reduce the cellular network resource consumption and associated costs, achieving, at the same time, a complete containment of the malware in a limited amount of time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
