基于遭遇的蠕虫：分析与防御
Encounter-based worms: Analysis and Defense
摘要：
相遇网络是一种频繁断开的无线自组织网络，需要近邻节点存储转发聚合数据以实现信息传播。使用网关或防火墙等传统方法阻止蠕虫在基于防火墙的网络中传播是不合适的。我们提出了蠕虫互动的方法，依赖于自动化的有益蠕虫生成，旨在减轻蠕虫在这样的网络中传播的问题。为了了解动态的蠕虫交互及其性能，我们数学模型的基础上，主要的蠕虫交互因素，包括蠕虫交互类型，网络特性，节点特性，使用常微分方程和分析它们对我们提出的指标的影响蠕虫交互。我们使用广泛的合成和跟踪驱动的模拟验证我们提出的模型。我们发现，所有的蠕虫相互作用的因素显着影响蠕虫的传播模式。例如，免疫线性地减少了易感节点的感染，而开关行为只影响感染的持续时间。使用实际的移动网络测量，我们发现，遇到突发，多组和非均匀。一般而言，来自迹线驱动模拟的趋势与模型一致。在这种情况下，免疫和及时部署似乎是最有效的对抗蠕虫攻击的方法，而在特定情况下，合作可能会有所帮助。这些发现提供了一些见解，我们希望这将有助于在未来基于蠕虫的网络中开发反蠕虫协议。
英文摘要：
Encounter-based network is a frequently-disconnected wireless ad-hoc network requiring immediate neighbors to store and forward aggregated data for information disseminations. Using traditional approaches such as gateways or firewalls for deterring worm propagation in encounter-based networks is inappropriate. We propose the worm interaction approach that relies upon automated beneficial worm generation aiming to alleviate problems of worm propagations in such networks. To understand the dynamic of worm interactions and its performance, we mathematically model worm interactions based on major worm interaction factors including worm interaction types, network characteristics, and node characteristics using ordinary differential equations and analyze their effects on our proposed metrics. We validate our proposed model using extensive synthetic and trace-driven simulations. We find that, all worm interaction factors significantly affect the pattern of worm propagations. For example, immunization linearly decreases the infection of susceptible nodes while on-off behavior only impacts the duration of infection. Using realistic mobile network measurements, we find that encounters are bursty, multi-group and non-uniform. The trends from the trace-driven simulations are consistent with the model, in general. Immunization and timely deployment seem to be the most effective to counter the worm attacks in such scenarios while cooperation may help in a specific case. These findings provide insight that we hope would aid to develop counter-worm protocols in future encounter-based networks.
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