Technology Category
Machine Learning: Auto ML and Hyperparameter TuningHumans and AI: Human-in-the-loop Machine LearningNatural Language Processing: Learning & Optimization for NLP
Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection
🤖 AI Summary
This study addresses the low detection accuracy and high false positive rate prevalent in machine learning–based network intrusion detection systems (ML-NIDS). Using the KDD Cup 1999 dataset under a unified experimental framework, we systematically evaluate and optimize 14 ML classifiers. Innovatively, we perform simultaneous hyperparameter tuning—via both grid search and random search—for SVM, XGBoost, Random Forest, and ANN, integrated with 10-fold cross-validation and recursive feature elimination (RFE) to enhance generalization. Results show that the optimized SVM achieves 99.12% accuracy and a false positive rate of 0.0091—improving accuracy by 1.04 percentage points and reducing false positives by 26% over its default configuration—outperforming all other models. This work empirically validates that rigorous hyperparameter optimization and feature selection are critical for boosting ML-NIDS performance, providing a reproducible, methodology-driven foundation for developing high-reliability, lightweight intrusion detection systems.
Technology Category
Application Category
📝 Abstract
Network Intrusion Detection Systems (NIDS) are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. This study explores the application of machine learning (ML) methods to improve the NIDS accuracy through analyzing intricate structures in deep-featured network traffic records. Leveraging the 1999 KDD CUP intrusion dataset as a benchmark, this research evaluates and optimizes several ML algorithms, including Support Vector Machines (SVM), Naïve Bayes variants (MNB, BNB), Random Forest (RF), k-Nearest Neighbors (k-NN), Decision Trees (DT), AdaBoost, XGBoost, Logistic Regression (LR), Ridge Classifier, Passive-Aggressive (PA) Classifier, Rocchio Classifier, Artificial Neural Networks (ANN), and Perceptron (PPN). Initial evaluations without hyper-parameter optimization demonstrated suboptimal performance, highlighting the importance of tuning to enhance classification accuracy. After hyper-parameter optimization using grid and random search techniques, the SVM classifier achieved 99.12% accuracy with a 0.0091 False Alarm Rate (FAR), outperforming its default configuration (98.08% accuracy, 0.0123 FAR) and all other classifiers. This result confirms that SVM accomplishes the highest accuracy among the evaluated classifiers. We validated the effectiveness of all classifiers using a tenfold cross-validation approach, incorporating Recursive Feature Elimination (RFE) for feature selection to enhance the classifiers accuracy and efficiency. Our outcomes indicate that ML classifiers are both adaptable and reliable, contributing to enhanced accuracy in systems for detecting network intrusions.
Problem
Research questions and friction points this paper is trying to address.
Optimizing machine learning algorithms for network intrusion detection accuracy
Enhancing NIDS performance through hyperparameter tuning and feature selection
Evaluating SVM's superior classification accuracy after optimization on KDD dataset
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hyperparameter tuning enhances SVM accuracy for intrusion detection
Grid and random search optimize machine learning classifier performance
Recursive Feature Elimination improves classifier efficiency and accuracy
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