基于新奇检测的网络蠕虫预测
Predicting the Presence of Internet Worms using Novelty Detection
摘要：
互联网蠕虫每年造成数十亿美元的损失，影响全球数百万用户。为了及时部署对策，有必要使用自动化系统来检测蠕虫的传播。本文讨论了一种方法来确定一个蠕虫的存在，根据路由信息，目前可从互联网路由器。自动编码器是一种特殊类型的神经网络，用于检测正常路由行为中的异常。自动编码器使用来自单个路由器的信息进行训练，并且能够检测由蠕虫引起的全局不稳定以及局部路由不稳定。
英文摘要：
Internet worms cause billions of dollars in damage yearly, affecting millions of users worldwide. For countermeasures to be deployed timeously, it is necessary to use an automated system to detect the spread of a worm. This paper discusses a method of determining the presence of a worm, based on routing information currently available from Internet routers. An autoencoder, which is a specialized type of neural network, was used to detect anomalies in normal routing behavior. The autoencoder was trained using information from a single router, and was able to detect both global instability caused by worms as well as localized routing instability.
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。
