Improving Network Security with Intrusion Detection Systems Utilizing Machine Learning and Deep Learning Techniques
Mohammad Imtiaz Faisal, Md. Shamiul Islam · 2023
In an era of ubiquitous connectivity and enterprise digital transformation, network security against increasing cyber threats is crucial. Network Security Intrusion Detection Systems (NSIDS) are essential for network security. Traditional rule-based IDS struggles to keep up with cyberattacks changing terrain. This research integrates advanced Machine Learning (ML) and Deep Learning (DL) Various strategies to enhance or optimize NSIDS intrusion detection and mitigation. This study evaluates multiple ML and DL models using the NSL-KDD dataset, widely recognized as a standard for intrusion detection. Logistic Regression, k-nearest Neighbors, Gaussian Naive Bayes, Linear Support Vector classifiers, Decision Trees, Random Forests, PCA PCA RandomForest, custom neural network architectur and LSTM model networks are tested to classify network traffic as usual or attack. Ensemble learning approaches are also investigated to use several intelligence models to increase detection accuracy. The study technique includes data preparation, feature selection, model training, hyperparameter tuning, cross-validation and performance metric evaluation. The inquiry examines imbalanced data and network traffic data ethics. The results show the pros and cons of several ML and DL intrusion detection methods across different categories. The benefits of using these techniques in ensemble contexts are also discussed. A complete overview of recent intrusion detection algorithms and their practical use in network security is presented in the paper. This research emphasizes the importance of ML and DL in network security. It advises enterprises wishing to create more flexible and effective Intrusion Detection Systems in a dangerous digital world.