基于分布式学习的神经网络入侵检测算法研究
发布时间：2018-12-15 12:45
【摘要】：当今社会，计算机网络发展迅速，确保网络信息的安全性就显得尤为重要。能够主动保护信息安全的入侵检测技术，作为一种保障措施而备受关注。神经网络的优势在于，它能够作为一种方法应用到入侵检测中。通过分析和训练大量的实例数据，神经网络学习训练的知识，根据已有的实例，自主掌握并分析出系统中各个实例和变量之间的关系，而不需要了解数据分布和解析的细节。 本文主要对入侵检测的概念、功能以及检测方法进行详细的介绍，并详细阐述了神经网络的概念、工作原理以及神经网络的研究内容。重点介绍了BP算法的原理、步骤以及流程，根据BP神经网络模型的特点，通过比较算法的优缺点，对现有算法进行改进。 首先，从神经网络的原理入手，对该原理进行讨论，研究了传统BP网络学习算法，并结合分布式和自适应的特点，对传统BP算法进行改进，提出了一种优化的BP神经网络入侵检测算法，即分布式神经网络自学习算法。通过改进的算法对入侵数据进行检测和学习，直接使用BP学习方法的训练样本数量过大而且不易收敛，这一问题得到了很好地解决。 其次，通过对改进算法的研究，给出算法的具体步骤，并运用改进的算法来建立模型，对该模型进行分析，与传统BP网络学习算法进行对比，，验证改进算法的可行性与有效性。 最后，将算法应用于入侵检测中，通过相应的测试方法，对本文所采用的样本数据集来进行实例验证。通过检测数据的测试结果，验证分布式神经网络自学习算法的性能，得出结论。
[Abstract]:Nowadays, with the rapid development of computer network, it is very important to ensure the security of network information. Intrusion detection technology, which can actively protect information security, has attracted much attention as a safeguard. The advantage of neural network is that it can be applied to intrusion detection as a method. By analyzing and training a large number of case data, neural networks learn the knowledge of training, according to the existing examples, independently grasp and analyze the relationship between each instance and variables in the system, without the need to understand the details of data distribution and analysis. In this paper, the concept, function and detection method of intrusion detection are introduced in detail, and the concept, working principle and research content of neural network are described in detail. The principle, steps and flow of BP algorithm are introduced emphatically. According to the characteristics of BP neural network model, the advantages and disadvantages of the algorithm are compared and the existing algorithms are improved. Firstly, this paper discusses the principle of neural network, studies the traditional learning algorithm of BP network, and combines the characteristics of distributed and adaptive, improves the traditional BP algorithm. An optimized BP neural network intrusion detection algorithm, distributed neural network self-learning algorithm, is proposed. By using the improved algorithm to detect and learn intrusion data, the number of training samples using BP learning method is too large and difficult to converge. This problem has been solved well. Secondly, through the research of the improved algorithm, the concrete steps of the algorithm are given, and the model is established by using the improved algorithm, and the model is analyzed, and compared with the traditional BP network learning algorithm, the feasibility and effectiveness of the improved algorithm are verified. Finally, the algorithm is applied to intrusion detection, and the sample data set used in this paper is verified by the corresponding test method. The performance of the distributed neural network self-learning algorithm is verified by the test results of the data, and the conclusion is drawn.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.08;TP183
本文编号：2380651
[Abstract]:Nowadays, with the rapid development of computer network, it is very important to ensure the security of network information. Intrusion detection technology, which can actively protect information security, has attracted much attention as a safeguard. The advantage of neural network is that it can be applied to intrusion detection as a method. By analyzing and training a large number of case data, neural networks learn the knowledge of training, according to the existing examples, independently grasp and analyze the relationship between each instance and variables in the system, without the need to understand the details of data distribution and analysis. In this paper, the concept, function and detection method of intrusion detection are introduced in detail, and the concept, working principle and research content of neural network are described in detail. The principle, steps and flow of BP algorithm are introduced emphatically. According to the characteristics of BP neural network model, the advantages and disadvantages of the algorithm are compared and the existing algorithms are improved. Firstly, this paper discusses the principle of neural network, studies the traditional learning algorithm of BP network, and combines the characteristics of distributed and adaptive, improves the traditional BP algorithm. An optimized BP neural network intrusion detection algorithm, distributed neural network self-learning algorithm, is proposed. By using the improved algorithm to detect and learn intrusion data, the number of training samples using BP learning method is too large and difficult to converge. This problem has been solved well. Secondly, through the research of the improved algorithm, the concrete steps of the algorithm are given, and the model is established by using the improved algorithm, and the model is analyzed, and compared with the traditional BP network learning algorithm, the feasibility and effectiveness of the improved algorithm are verified. Finally, the algorithm is applied to intrusion detection, and the sample data set used in this paper is verified by the corresponding test method. The performance of the distributed neural network self-learning algorithm is verified by the test results of the data, and the conclusion is drawn.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.08;TP183
【引证文献】
相关硕士学位论文 前2条
1 赵菁伟;基于分簇Ad Hoc网络的入侵检测系统设计[D];河北科技大学;2016年
2 许锋;人工神经网络与遗传算法相结合的入侵检测模型的研究[D];江苏科技大学;2015年
本文编号：2380651
本文链接：https://www.wllwen.com/guanlilunwen/ydhl/2380651.html
