期刊名称：International Journal of Computer Networks & Communications
印刷版ISSN：0975-2293
电子版ISSN：0974-9322
出版年度：2020
卷号：12
期号：6
页码：117-131
DOI：10.5121/ijcnc.2020.12608
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Today's Internet and enterprise networks are so popular as they can easily provide multimedia and ecommerce services to millions of users over the Internet in our daily lives. Since then, security has been a challenging problem in the Internet's world. That issue is called Cyberwar, in which attackers can aim or raise Distributed Denial of Service (DDoS) to others to take down the operation of enterprises Intranet. Therefore, the need of applying an Intrusion Detection System (IDS) is very important to enterprise networks. In this paper, we propose a smarter solution to detect network anomalies in Cyberwar using Stacking techniques in which we apply three popular machine learning models: k-nearest neighbor algorithm (KNN), Adaptive Boosting (AdaBoost), and Random Decision Forests (RandomForest). Our proposed scheme uses the Logistic Regression method to automatically search for better parameters to the Stacking model. We do the performance evaluation of our proposed scheme on the latest data set NSLKDD 2019 dataset. We also compare the achieved results with individual machine learning models to show that our proposed model achieves much higher accuracy than previous works.
