Performance Evaluation of an Artificial Intelligence and Neural Network-Based Computer Network Security Defence System
Keywords:
artificial intelligence; neural network; intrusion detection; CICIDS2017; network security; machine learningAbstract
The escalating sophistication and volume of cyberattacks targeting modern computer networks have exposed the limitations of traditional, signature-based intrusion detection mechanisms, motivating growing interest in artificial intelligence (AI) and neural network-based defence systems. This study evaluates the performance of an AI-driven, neural network-based computer network security defence system using the CICIDS2017 benchmark dataset, which comprises more than 2.8 million labelled network-flow observations covering benign traffic and fourteen contemporary attack categories. The research methodology encompassed data pre-processing, including duplicate removal, treatment of missing and infinite values, categorical encoding, and Min-Max normalisation, followed by feature selection and stratified partitioning of the dataset into training, validation, and testing subsets. A supervised neural network classifier was trained on the processed data and evaluated using accuracy, precision, recall, F1-score, false-positive rate, false-negative rate, detection rate, and response time. The results indicate that pre-processing substantially improved data quality, with approximately 10.95% of the original observations removed as duplicate or invalid records, and that the trained model achieved strong discriminative performance in separating benign from malicious traffic while maintaining a comparatively low false-positive rate. The findings confirm that a carefully pre-processed dataset, combined with an appropriately configured neural network architecture, provides an effective and scalable approach to modern intrusion detection, while also underscoring the continuing methodological importance of class-imbalance management in cybersecurity machine-learning research. The study contributes an integrated empirical framework linking dataset characteristics, pre-processing decisions, and classification outcomes for AI-based network security defence systems.
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