基于集成深度森林的入侵检测方法

基于卷积神经网络(CNN)的入侵检测方法在实际应用中模型训练时间过长、超参数较多、数据需求量大。为降低计算复杂度,提高入侵检测效率,提出一种基于集成深度森林(EDF)的检测方法。在分析CNN的隐藏层结构和集成学习的Bagging集成策略的基础上构造随机森林(RF)层,对每层中RF输入随机选择的特征进行训练,拼接输出的类向量和特征向量并向下层传递迭代,持续训练直至模型收敛。在NSL-KDD数据集上的实验结果表明,与CNN算法相比,EDF算法在保证分类准确率的同时,其收敛速度可提升50%以上,证明了EDF算法的高效性和可行性。

Intrusion Detection Method Based on Ensemble Deep Forests

In practical application,the Convolutional Neural Network(CNN)-based intrusion detection method has some problems,such as long training time,a large number of hyper parameters,and a large amount of required data.In order to reduce the complexity of training and improve the efficiency of intrusion detection,this paper proposes an detection method based on Ensemble Deep Forests(EDF).On the basis of analyzing the hidden layer structure of CNN and the Bagging integration strategy of ensemble learning,the method constructs a Random Forest(RF)layer.Then the features randomly selected by the RF input are trained in each layer.The output class vectors and feature vectors are spliced,and iterations are passed to the next layer.Training continues until the model converges.Experimental results on the NSL-KDD dataset show that compared with the CNN algorithm,the EDF algorithm can improve the convergence speed by more than 50%while ensuring the classification accuracy,which proves the efficiency and feasibility of the EDF algorithm.