Machine learning-based intrusion detection framework using NSL-KDD dataset
TL;DR
SVM-based models with Gaussian and Sigmoid kernels are particularly more accurate (up to 99%) and less sensitive to fluctuations and made flexible technical solutions for effectively managing large and dynamically changing networks.
Abstract
Applying Artificial Intelligence (AI) to the context of networking systems has greatly enhanced the ability to detect, protect, and manage the kind of flows in networks in real time. This research explores the relationship between AI and networking, and more particularly, the extent to which AI technology can be used to enhance, protect, and scale the NIDS using the NSL-KDD dataset. This research focuses on the following objectives: (a) enhancing the performance of network intrusion detection models, and (b) strengthening the defense mechanisms against dangerous new forms of cyber threats, and (c) making flexible technical solutions for effectively managing large and dynamically changing networks. To address these objectives, different classification algorithms such as SVM of different kernels, KNN, Random Forest, and Decision trees are applied on the NSL-KDD dataset. Testing accuracy, precision, recall, F1 score, and cross-validation score are used to evaluate them. In addition, feature extraction algorithms, including both fuzzy and correlation-based methods, are applied to reduce the model’s computational complexity. Targets of the investigation include the evaluation of each model and feature extraction, and its impact on the model’s ability to predict. The results affirm that, compared with other methods, SVM-based models with Gaussian and Sigmoid kernels are particularly more accurate (up to 99%) and less sensitive to fluctuations.