一种基于流量统计信息的网络蠕虫检测方法及其应用的开题报告VIP专享

精品文档---下载后可任意编辑一种基于流量统计信息的网络蠕虫检测方法及其应用的开题报告摘要：网络蠕虫病毒是目前互联网攻防领域最为常见的黑客工具之一，它通过利用网络上的漏洞和弱口令进行自我传播，给网络安全带来了巨大的威胁。因此，进展一种高效、准确的网络蠕虫检测方法，对于维护网络安全具有重要意义。本文提出一种基于流量统计信息的网络蠕虫检测方法。本方法首先采纳传统的流量特征分析方法对网络流量进行实时分析，获得流量的各种统计信息，进而对其中存在的网络蠕虫行为进行检测。具体来说，本方法用 kNN算法对网络流量进行分类，通过训练一定数量的有标记流量数据，对未知流量进行分类，推断其是否为网络蠕虫。本方法的实验结果表明，在使用 CICIDS2024 和 UNSW-NB15 两个数据集进行实验时，该方法的准确率和 F1 值均达到了较高水平，比传统的蠕虫检测方法更加准确和高效。本方法将有望应用于网络安全领域中的蠕虫检测、网络入侵检测等方面。关键词：网络蠕虫检测；流量分析；kNN 算法Abstract:Internet worm virus is currently one of the most common hacker tools in the Internet attack and defense field. It spread itself by exploiting vulnerabilities and weak passwords on the network, which poses a great threat to network security. Therefore, developing an efficient and accurate network worm detection method is of great significance for maintaining network security. This paper proposes a network worm detection method based on traffic statistical information.This method first uses the traditional traffic feature analysis method to perform real-time analysis on network traffic, obtains various statistical information of traffic, and then detects the network worm behavior among them. Specifically, this method uses the kNN algorithm to classify network traffic, and classifies unknown traffic by training a certain amount of labeled traffic data to determine whether it is a network worm.Experimental results show that, when using the CICIDS2024 and UNSW-NB15 datasets for experiments, the accuracy and F1 values of this method have reached a high level, which is more accurate and efficient than traditional worm detection methods.This method is expected to be applied to worm detection, network intrusion detection and other aspects in the field of network security.Keywords: network worm detection; traffic analysis; kNN algorithm