PROACTIVE CYBER DEFENSE: A FAR-REACHING STUDY ON REAL-TIME INTRUSION DETECTION FRAMEWORKS USING MACHINE LEARNING

Abstract Real-time IDS are pivotal for protecting modern networks inimical to increasingly sophisticated and rapidly evolving cyber threats. This research explores the development and evaluation of a real-time IDS utilizing machine learning algorithms. “The system leverages the power of ml to put one’s finger on and analyse network traffic malicious activities with minimal latency. We investigate various machine learning models, including mention specific models used, e.g., deep learning, ensemble methods, and evaluate their recital in terms of rigor, sensing outlay, false positive outlay, and processing speed”. Via proposed survey research is use designed to manoeuvre voluminous network snarl up along with adapt onto emerging threats through continuous learning and model updates. This study achieves a soaring detection outlay although perpetuate a stubby false positive outlay and meeting real-time performance requirements. This survey research contributes to the ongoing effort to enhance network security by providing a robust and adaptable real-time IDS solution based on machine learning.