Integration of artificial intelligence techniques and intrusion detection systems in anomaly detection for data networks

This research investigates the integration of artificial intelligence (AI) and intrusion detection system (IDS) for anomaly detection in data networks, the study aims to evaluate the effectiveness of AI techniques in identifying malicious activities within network traffic. A proprietary dataset is developed instead of other existing ones to have a varied set of threats and mainly due to the architecture used to realize the analysis. The integration of AI with IDS represents a significant advancement in the field of cybersecurity, particularly in the context of increasing cyber threats identification. Through a comprehensive analysis involving the use of open-source software tools and various data mining models, this study demonstrates that AI-enhanced IDS systems can significantly improve the detection of network anomalies, thereby enhancing overall network security. The results highlight the critical need for continuous updates and the adaptation of new AI models to address the evolving cyber threats that frequently occur.