Network anomaly detection using deep learning techniques

作     者：Mohammad Kazim Hooshmand Doreswamy Hosahalli 

作者机构：Department of Computer ScienceMangalore UniversityMangaloreIndia 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2022年第7卷第2期

页      面：228-243页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：artificial intelligence convolution neural network security 

摘      要：Convolutional neural networks(CNNs)are the specific architecture of feed-forward artificial neural *** is the de-facto standard for various operations in ma-chine learning and computer *** transform this performance towards the task of network anomaly detection in cyber-security,this study proposes a model using one-dimensional CNN *** authors approach divides network traffic data into transmission control protocol(TCP),user datagram protocol(UDP),and OTHER protocol categories in the first phase,then each category is treated *** training the model,feature selection is performed using the Chi-square technique,and then,over-sampling is conducted using the synthetic minority over-sampling technique to tackle a class imbalance *** authors method yields the weighted average f-score 0.85,0.97,0.86,and 0.78 for TCP,UDP,OTHER,and ALL categories,*** model is tested on the UNSW-NB15 dataset.