摘要：

为了有效解决海量复杂数据的入侵检测分类问题,基于深度信念网络(DBN)和极限学习机(ELM),提出一种新的入侵检测方法。使用DBN对大量复杂无标签的原始数据进行特征提取,得到高度抽象的重要特征,再用ELM完成最终的分类工作。结合DBN自动提取特征的能力和ELM快 速学习且泛化性好的优势,提高入侵检测识别率和运行效率。实验结果表明,与原始的DBN、ELM以及DBN-SVM方法相比,该方法具有更优的精确度和运行效率。

关键词: 深度学习, 深度信念网络, 极限学习机, 混合模型, 入侵检测, 无监督

Abstract:

In order to effectively solve the classification performance of massive and complex intrusion detection data,an intrusion detection method of hybrid deep learning model is proposed,which is based on Deep Belief Network (DBN) and Extreme Learning Machine(ELM).DBN-ELM uses DBN to extract features from the massive,complex and unlabeled data to get highly abstract features at first,and then takes ELM as the classifier to finish the last classification.It totally improves the recognition rate of intrusion detection and the efficiency of the algorithm operation because it combines the ability of DBN to automatically extract features and fast learning and good generalization of ELM.Compared with DBN,ELM and DBN-ELM,experiments on KDD99 and NSL-KDD dataset show that DBN-ELM has better accuracy and efficiency of algorithm.

Key words: deep learning, Deep Belief Network(DBN), Extreme Learning Machine(ELM), hybrid model, intrusion detection, unsupervised

中图分类号: 

• TP391