A Comprehensive Evaluation of AI-Driven Data Science Models in Cybersecurity: Covering Intrusion Detection, Threat Analysis, Intelligent Automation, and Adaptive Decision-Making Systems

Abstract

The exponential growth of cyber threats has necessitated a paradigm shift from traditional signature-based security mechanisms to sophisticated artificial intelligence-driven approaches capable of adapting to evolving attack vectors. This study presents a comprehensive evaluation of contemporary AI and data science models across four critical cybersecurity domains: intrusion detection systems, threat analysis, intelligent automation, and adaptive decision-making frameworks. We systematically evaluate multiple machine learning architectures including deep neural networks, ensemble methods, and reinforcement learning algorithms using benchmark datasets NSL-KDD, CICIDS2017, and UNSW-NB15. Our empirical analysis reveals that hybrid models combining convolutional neural networks with long short-term memory architectures achieve superior performance in sequential attack pattern recognition, attaining accuracy rates exceeding 98.3% while maintaining acceptable false positive rates below 1.2%. Furthermore, transformer-based models demonstrate remarkable capabilities in natural language processing for threat intelligence extraction, while reinforcement learning agents show promising adaptability in dynamic response scenarios despite computational overhead constraints. The comparative framework developed herein provides practitioners with evidence-based guidance for model selection tailored to specific organizational contexts, security requirements, and computational resources. This work bridges the gap between theoretical AI research and practical cybersecurity implementation, offering actionable insights for security operations centers facing real-world deployment challenges in increasingly hostile digital environments.