Performance Evaluation of Intrusion Detection System Using Machine Learning and Deep Learning Algorithms

Abstract

Now that Internet access is so widely used, our society has a greater number of networked technologies. Data travels between them because of their daily activities. Due to the server's weaknesses, hackers may get access to the system through difficult-to-identify network breaches. One of the most well-known defense mechanisms against these attacks on networked devices is the Intrusion Detection System (IDS), which is built into the system. IDS has previously received extensive training in the classification of threats using traditional machine learning-based models and pre-assembled datasets. In this research, we presented two deep learning-based models, the Multilayer Perceptron Model (MLP) and Long-Short Term Memory (LSTM), along with five machine learning-based models, including Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM). The NSL-KDD dataset has been used to achieve 89.6% accuracy with normalization and 89.2% without normalization, 97.77% with LSTM and 96.89% with MLP. Each record in the data collection has 43 features, including two labels and 41 features that are related to traffic input.