Towards Detection of Network Anomalies using Machine Learning Algorithms on the NSL-KDD Benchmark Datasets

In the present era, everyone is connected via the Internet for sharing digital information. The digital data is stored using the cloud technology. However, cloud technology is speedily increasing the volume of digital information and network intrusions. In this case, safeguarding the cloud data is essential for several purposes. Therefore, the present study emphasizes developing the network intrusion detection system using the benchmark NSL-KDD datasets. The ensemble learning-enabled random forest algorithm was proposed and implemented to select the most suitable features. The network intrusion detection and classification have been done using three machine learning models: support vector machine (SVM), logistic regression, and K-nearest neighbour's (KNN) with 87.58, 88.86, and 98.24% validation accuracies. Thus, the present study approach can be used in real-time cyber-attack detection and monitoring.