The Role Of Artificial Intelligence In Autonomous Cyber Threat Detection For Business Systems
Azad, Md Abul Kalam, Zubair, K M, Asha, Nurtaz Begum, Khan, Akhtaruzzaman, Rimon, Rakib Hassan · Zenodo (CERN European Organization for Nuclear Research) · 2023
This study investigates the use of Artificial Intelligence (AI) for autonomous cyber threat detection in modern business systems. Traditional security systems based on fixed rules and signatures struggle to detect evolving and sophisticated cyber attacks. To address this, AI-driven analytical models using machine learning and deep learning are applied to identify anomalies, predict intrusion patterns, and respond to threats proactively. The study uses the CICIDS2017 reduced dataset to simulate realistic network traffic containing both benign and malicious flows. Python was used for preprocessing and model training, Excel for statistical aggregation, and Tableau for visualizing attack distributions, flow behaviors, and packet relationships. Findings show that AI-based detection frameworks significantly outperform traditional mechanisms, particularly for DoS, brute-force, and port-scanning attacks. The results highlight the importance of combining quantitative analysis with intelligent visualization for enhanced situational awareness and proactive threat prevention. The study demonstrates the adaptability and scalability of AI-driven cybersecurity systems and the need for continuous learning models capable of evolving with dynamic cyber threats. Originally published in: Innovation in the Economy (ISSN: 2181-9491), Volume 42, Issue 32, Pages 884–908, on December 28, 2023.