IDS with hybrid sampling technique: combination over and under-sampling technique and comparison with deep convolutional approach

Abstract

Digital is constantly evolving with the appearance of connected objects and on top of the popularization today of artificial intelligence. One of the direct inductions remains the excessive proliferation of various kinds of attacks in computer systems. Hackers exploit these vulnerabilities to break in and attack systems with increasingly complex attacks. The consequences of intrusions are destructive and ruinous for businesses and organizations such as electronic ransom ware, data alteration and loss, financial and brand image loss.It is important for those involved in computer systems to equip any computer centre with adequate tools to prevent malicious individuals from accessing the systems. To remedy these setbacks, several IT tools are developed including IDS intrusion detection systems. IDS intrusion detection systems are devices designed to monitor a computer system, give alerts and trigger real-time counterattacks in the event of attacks. These intelligent systems use several detection approaches and various algorithms. The performance of the IDS is increased when the features dimensionality are reduced significantly.This study proposed feature dimensionality reduction techniques such as Principal Component Analysis (PCA) and  Auto-Encoder (AE). The output from the reduced dimensional features are used to build machine Learning algorithms.  The performance results is evaluated on the  CSECICIDS2018 datasets.  The proposed public intrusion data sets suffer from the Imbalance class. In order to handle this issue, we propose hybrid sampling technique by combining Over and undersampling technique.The performance results from the reduced features in terms of true positive, False positve, recall, precision, F-Measure, ROC Area, PRC Area show the better performance. In addition, the obtained results are compared with deep convolutional approach.

Keywords: Machine learning, Principal Component Analysis, intrusion detection system, artificial neural network, deep learning, CSECICIDS2018 datasets, hybrid sampling techniqueMachine learning, Principal Component Analysis, IDS, Artificial neural network, deep learning, CSECICIDS2018, hybrid sampling technique

DOI: 10.54941/ahfe1004488