基于网络多入侵行为识别的数学建模分析研究

Research on Mathematical Modeling and Analysis of Network Multi-Intrusion Behavior Recognition

摘要: 在互联网飞速发展的同时，网络的安全性也越来越突出。网络入侵行为对信息系统和数据安全构成了严重威胁，因此网络入侵行为的识别和分析成为了网络安全领域的重要研究方向之一。本文以网络多入侵行为识别为研究对象，通过对入侵行为的数学建模分析，旨在提高网络安全防护水平。本文对网络入侵行为进行了分类和定义，明确了入侵行为的特征和类型。基于已有的入侵行为数据集，采用数学建模的方法对入侵行为进行了分析和建模。通过对入侵行为的数据特征提取和模式识别，建立了入侵行为的数学模型，并提出了相应的识别算法。在数学建模分析的基础上，本文还对网络多入侵行为的识别技术进行了探讨和研究。通过对入侵行为的特征提取和数据挖掘技术的应用，提出了一种基于网络流量和行为分析的入侵行为识别方法，并进行了实验验证和性能评估。研究指出了网络多入侵行为识别技术的意义和应用前景，为进一步提高网络安全防护水平提供了理论和技术支持。

Abstract: With the rapid development of the Internet, the security of the network is also becoming more and more prominent. Network intrusion poses a serious threat to information system and data security, therefore, it is an important task to identify and analyze network intrusion. This paper takes the recognition of network intrusion behavior as the research object, and aims to improve the level of network security protection through the mathematical modeling and analysis of intrusion behavior. In this paper, the network intrusion behavior is classified and defined, and the characteristics and types of intrusion behavior are defined. Based on the existing intrusion behavior data set, the intrusion behavior is analyzed and modeled by mathematical modeling method. Based on the data feature extraction and pattern recognition of intrusion behavior, the mathematical model of intrusion behavior is established, and the corresponding recognition algorithm is proposed. On the basis of mathematical modeling analysis, this paper also discusses and researches the recognition technology of network multi-intrusion behavior. Through the application of feature extraction and data mining technology of intrusion behavior, this paper presents a new approach to IDS based on network flow and behavior analysis, and verifies it and evaluates its performance. The research points out the research significance and application prospect of network multi-intrusion behavior identification technology, and provides theoretical and technical support for further improving the level of network security protection.