Intelligent Network Traffic Identification Based on Advanced Machine Learning Approaches
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P113Keywords:
Cybersecurity, Network Intrusion Detection, RNN, Deep Learning, Machine LearningAbstract
Network Traffic Analysis (NTA) refers to the process whereby the traffic of the network is logged and analyzed, to identify security risks or performance issues. There are, however, instances when machine learning (ML) is applied in the mechanization of NTA. Network information may be categorized, anomalies detected, and malicious actions identified using machine learning. They could also be employed in foreseeing the same traffic patterns in the future, which can be exploited and utilized to improve network performance. The article gives a recurrent neural network (RNN)-based intrusion detection system (IDS) on the NSL-KDD dataset. The methodology consists of a lengthy preprocessing stage, which involves treating missing values, performing dimensionality reduction, applying one-hot encoding, normalizing the data, selecting features, as well as dividing the training and test sets. The RNN is then trained to identify sequential dependencies in network traffic, enabling it to distinguish between malicious and legitimate activities. These experimental findings show that the suggested model has the following values: F1-score of 99.97%, accuracy of 99.98%, precision of 99.98%, and recall of 99.99% when compared with more traditional ML models like Naive Bayes (NB) and Support Vector Machine (SVM). These findings prove that RNN is efficient and applicable to skewed classes and multidimensional time series patterns in network intrusion detection. The study also identifies the possibility that deep learning (DL) solutions can be used to scale up IDS and enhance its accuracy in real-world network systems
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