基于联邦学习和卷积神经网络的入侵检测方法

An Intrusion Detection Method Based on Federated Learning and Convolutional Neural Network

目前基于深度学习的入侵检测算法是入侵检测研究领域的研究热点,但是大多数研究的重点集中在如何改进算法来提高入侵检测的准确率,而忽视了实际中单个机构所产生的有限的标签数据不足以训练出一个高准确率的深度模型的问题。文章提出一种基于联邦学习和卷积神经网络的入侵检测方法,可以通过多个参与方的数据集联合训练模型达到扩充数据量的目的。该方法利用联邦学习框架,设计了基于深度学习的入侵检测模型。首先通过数据填充进行数据维度重构,形成二维数据,然后在联邦学习的机制下利用DCNN网络进行特征提取学习,最后结合Softmax分类器训练模型进行检测。实验结果表明,该方法很大程度上减少了训练时间并保持较高的检测率。另外,与一般的入侵检测模型相比,该模型还保证了数据安全隐私。

At present,intrusion detection based on deep learning is a hot topic in the field of intrusion detection,but most of the research focuses on how to improve the algorithm to improve the accuracy of intrusion detection,while neglecting that the limited label data generated by a single mechanism is not enough to train a depth model with high accuracy.In this paper,an intrusion detection method based on federated learning and convolution neural network is proposed,which can expand the amount of data through the joint training model of multiple participants.In this method,an intrusion detection model of deep learning is designed by using federated learning framework.Firstly the data dimension is reconstructed to form two-dimensional data through data filling,and then the feature extraction learning is carried out by using DCNN network under the mechanism of federated learning.Finally,the training model of softmax classifier is combined and detected.The experimental results show that the method reduces the training time to a great extent and maintains a high detection rate.In addition,compared with the general intrusion detection model,the model also ensures the security and privacy of the data.