摘要
如今,计算机安全非常重要,对于网络管理员或安全人员而言,要检测出正在攻击的计算机以及入侵源非常困难。伴随人工神经网络的入侵检测系统的出现,此类攻击的检测变得更加有效。神经网络具有学习以及能够对数据进行分类的优势,应用弹性传播神经网络检测模拟攻击的研究成为一项重要的手段。该文提出的研究方法包含数据预处理模块和神经网络模块,数据预处理模块执行归一化数据功能,而神经网络则对每个连接进行处理和分类以找出攻击。经过文中研究方法的结果与现有方法进行比较,我们可以发现人工神经网络检测具备很好的优势。
Today, computer security is very important. It is very difficult for network administrators or security personnel to detect the computer being attacked and the source of intrusion. With the emergence of intrusion detection systems based on artificial neural networks, the detection of such attacks has become more effective. Neural networks have the advantages of learning and being able to classify data. The application of elastic propagation neural networks to detect simulated attacks has become an important method. The research method proposed in this paper includes a data preprocessing module and a neural network module. The data preprocessing module performs the function of normalizing data, while the neural network processes and classifies each connection to find the attack. After comparing the results of the research method in the article with the existing methods, we can find that artificial neural network detection has good advantages.
作者
郑晓坤
ZHENG Xiao-kun(Department of Information Engineering,Yantai Gold College,Yantai 265401,China)
出处
《电脑知识与技术》
2021年第19期91-92,97,共3页
Computer Knowledge and Technology
关键词
计算机安全
人工神经网络
弹性传播
computer security
artificial neuralnetwork
elastic propagation
