First, we analyzed the feasibility of applying information entropy in the detection of network worms and conducted related tests.
首先分析了将信息熵应用于网络蠕虫检测的可行性，并进行了相关测试。
To improve the performance of parallel simulation of network worms, the simulation task of worms should be partitioned reasonably.
为提高并行网络蠕虫模拟的性能，需要对蠕虫模拟任务进行合理的划分。
Because of the huge damage of network worm epidemics, how to detect and response network worms has become an important task in computer network security field.
由于网络蠕虫泛滥产生的巨大危害，如何对网络蠕虫进行检测、预警和应对，已经成为计算机网络安全研究领域的一个重要课题。
The result of experiments shows that our adaptive worm detection algorithm can effectively detect the spread of network worms, and has a low false alarm rate and omission rate.
实验结果表明，本文所设计的自适应蠕虫检测算法能够有效的检测网络蠕虫的传播，且具有较低的误报率和漏报率。
With the popularity of network applications, network worms become the focus of attention, how to detect worms and how to prevent its spreading and destruction has became a research hotspot.
随着网络应用的普及，网络蠕虫病毒成为人们关注的焦点，如何检测并阻止蠕虫病毒的传播和破坏也成为研究的热点。
Network worms can not only exhaust system resources of infected hosts and damage them, but also occupy network bandwidth and thus jam network, even disrupt whole network.
网络蠕虫的传播不仅可以占用被感染主机的大部分系统资源，对目标系统造成破坏，同时，还会抢占网络带宽，造成网络严重堵塞，甚至整个网络瘫痪。
Network worms can not only exhaust system resources of infected hosts and damage them, but also occupy network bandwidth and thus jam network, even disrupt whole network.
网络蠕虫的传播不仅可以占用被感染主机的大部分系统资源，对目标系统造成破坏，同时，还会抢占网络带宽，造成网络严重堵塞，甚至整个网络瘫痪。
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