Analysis of IDS using Feature Selection Approach on NSL-KDD Dataset

Abstract

Due to the increased use of the internet, cyber-attacks are becoming more prominent causing major difficulty in achieving and preventing security risks and threats in the network. There have been a variety of attacks (both passive and aggressive) used to compromise network security and privacy. As a result, network security is becoming an increasingly important aspect in safe guarding and maintaining network data and resources to ensure dependable, secure access and protection against vulnerabilities. For detecting such attacks quickly and accurately, a strong Intrusion Detection System is required which is a valuable means for detecting intrusions in a network or system by extensively inspecting each packet in the network in real-time, preventing any harm to the user or system resources. In this paper, we proposed a statistical method to train the model with the training data to understand complicated patterns in the dataset and to make intelligent decisions or predictions whenever it comes across new or previously unseen data instances. For the classification of data, we used five machine learning classifiers such as Support Vector Machine, Decision Tree, Random Forest, AdaBoost, and Logistic Regression. To properly grasp complicated patterns in data, machine learning models require a large amount of data, which is why NSL-KDD was utilized to develop and validate supervised machine learning models. Initially, the dataset is pre-processed to remove any unnecessary or undesired dataset features. Feature selection (extra-treeclassifier) were used which combines the qualities of both filter and wrapper methods to provide features based on their importance as a result, the dataset dimensionality is reduced, lowering the processing complexity. Finally, the overall classification accuracy of the various machine learning classifiers was evaluated to find the best optimal algorithm for detecting intrusions.