@article{dziedzic2025artificial,
  title = {Artificial Intelligence-Based Intrusion Detection System: A Comparison with the Traditional Snort Solution},
  author = {Maurycy DZIEDZIC and Piotr KOZIKOWSKI and Maciej SOBIERAJ},
  year = 2025,
  url = {https://ibimapublishing.com/p-articles/46AI/2025/4641025/},
  journal = {Communications of International Proceedings},
  volume = 2025 (34),
  doi = doi.org/10.5171/2025.4641025,
  abstract = {This article presents a comparative analysis of an implemented modern Intrusion Detection System (IDS) based on artificial intelligence algorithms and traditional solutions, with particular emphasis on the Snort platform. The study includes a comprehensive evaluation of the effectiveness of various IDS/IPS systems in test environments, analyzing their ability to detect and block network and application-level attacks. The model based on the RandomForestClassifier algorithm achieved 99% accuracy, demonstrating high effectiveness in detecting UDP flood attacks. The comparison with Snort 3 system reveals significant differences in detection methods, operational efficiency, and practical aspects of implementation. The research results indicate the complementary nature of AI-based systems and traditional signature-based methods, suggesting the optimal use of both approaches depending on the environment’s characteristics and security requirements.},
  keywords = {intrusion detection system, artificial intelligence, machine learning, Snort, RandomForestClassifier, cybersecurity.},
  note = Article ID: 4641025
}