INTEGRATING DATA SCIENCE AND ARTIFICIAL INTELLIGENCE FOR NETWORK SECURITY: A COMPREHENSIVE REVIEW AND FRAMEWORK
Prachi S. Deshpande · INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING & TECHNOLOGY · 2020
The exponential increase in the scale, sophistication, and frequency of cyberattacks has rendered traditional rule-based security mechanisms insufficient for modern network environments.The convergence of Data Science and Artificial Intelligence (AI) offers a transformative pathway to enhance network resilience through automated detection, adaptive learning, and predictive defense.This paper presents a comprehensive review of the integration of data science and AI for network security, emphasizing their combined potential to enable intelligent, context-aware, and selfevolving defense systems.First, fundamental concepts of network security, data science processes, and AI models are summarized.Next, the contributions of each paradigm to cybersecurity are analyzed, highlighting their roles in intrusion detection, anomaly recognition, malware classification, and behavior analytics.The paper then proposes a conceptual Integrated Data-Driven Security Framework (IDDSF), unifying data preprocessing, feature engineering, machine learning, deep learning, and adaptive response layers.Challenges-including data imbalance, adversarial manipulation, explainability, and privacy-are critically examined, followed by an outlook on future research directions.This study highlights that the integration of data science and AI is Prachi Deshpande