Towards Intelligent Network Defense: A CNN-Driven Intrusion Detection Model

Abstract

This project explores an avant-garde approach employing CNNs to enhance intrusion detection capabilities, rigorous pre-processing involving a comprehensive descriptive analysis to elucidate data characteristics and anomalies. Data visualization techniques are utilized to map the distribution of intrusion types, revealing critical insights into class imbalances and guiding further pre-processing. This architecture integrates “Convolutional layers”, “Pooling layers”, and “Fully connected layers”. Model selection is a nuanced process, involving extensive experimentation to identify the most efficacious configuration. Binary cross-entropy is employed as the loss function apt for the binary classification of network activities. This metric quantifies the discrepancy between predicted and actual values. Performance is assessed through accuracy metrics on training and validation datasets. The project delivers a robust CNN-based intrusion detection system that showcases significant accuracy and resilience, leveraging advanced ML techniques to address the complexities of network security.