基于Focal Loss和卷积神经网络的入侵检测

入侵检测是信息安全防护领域中的一个重要环节。随着网络技术的发展，主动防御网络入侵变得越来越重要，同时入侵数据变得更加海量、复杂和不平衡，这导致传统的入侵检测技术的检测性能比较低，因此如何提高入侵检测系统的性能对于不平衡数据集的检测性能是一项巨大的挑战。传统的CNN模型对于处理复杂的数据具有很好的性能，但是在处理不平衡数据集上的效果不是很好。为了解决这个问题，提出一种基于Focal Loss和卷积神经网络的入侵检测方法。与传统的卷积神经网络不同，该模型利用Focal Loss损失函数来解决数据不平衡问题，并在卷积层加入正则化方法（DropBlock）用来提高模型的泛化能力。采用KDD 99数据集进行的实验表明，该模型入侵检测的准确率和精确率比传统的入侵检测模型有所提高。

Intrusion Detection Based on Focal Loss and Convolutional Neural Network

Intrusion detection is an important link in the field of information security protection. With the development of network technology, the active defense against network intrusions becomes more and more important, and intrusion data becomes more massive, complex, and unbalanced, which leads to the detection performance of the traditional intrusion detection technology is relatively low, so how to improve the detection performance of the intrusion detection system for unbalanced data sets is a huge challenge. The traditional CNN model has a good performance for processing complex data, but its effect of dealing with imbalanced data set is not very good. In order to solve this problem, an intrusion detection method based on Focal Loss and convolutional neural network is proposed. Different from traditional convolutional neural network, this model uses the Focal Loss function to solve the data imbalance problem, and in the convolutional layer, a regularization method (DropBlock) is added to improve the generalization ability of the model. Experiments of using KDD 99 data set show that the accuracy and precision of intrusion detection of this model are improved compared with the traditional intrusion detection model.