Network anomaly detection and performance evaluation of Convolutional Neural Networks on UNSW-NB15 dataset
Amol D. Vibhute, Minhaj Khan, Chandrashekhar H. Patil, Sandeep V. Gaikwad, Arjun V. Mane, Kanubhai K. Patel
Abstract
The present study uses the benchmark UNSW-NB15 datasets to detect network anomalies using the proposed deep learning-based Convolutional Neural Network (CNN) model. Several studies have already worked on network anomaly detection with some limits. However, earlier research has used old datasets and obtained limited accuracy. In addition, previous studies could not detect the latest malicious activities. Therefore, in the present study, firstly, we implemented the machine learning-based random forest method to diminish the dimensionality of the dataset and pick the most notable features. In this case, the random forest method has set only fifteen features from the forty-one and reduced the complexity of the data. Subsequently, the CNN model was trained and tested on the UNSW-NB15 dataset with 99.00% testing accuracy. The performance of the offered CNN model was assessed using precision, recall and the f1-values. The present study’s outcome can be used in real-time malicious activity detection.