A Survey on A Unified Web-Based Platform for Ransomware Detection and Network Intrusion Analysis

Cyberattacks have grown in sophistication with the emergence of advanced ransomware, zero-day payloads, and complex network intrusions. Existing security systems often focus only on detection, lacking comprehensive real-time response mechanisms. This survey explores the state of the art in AI-powered network monitoring, intrusion detection and prevention, ransomware detection, automated backup and recovery, and autonomous AI-driven ransom negotiation. By analyzing recent IEEE research on ransomware recovery [1], ML-based intrusion detection [2], proactive defense [3], network traffic analysis [4], anti-ransomware vulnerabilities [5], targeted ransomware mitigation [6], and Windows forensic investigations [7], this paper presents a unified framework that integrates machine learning, local large language models (LLMs) via Ollama, and automated self-healing processes. The proposed architecture offers a scalable, privacy-preserving, and intelligent approach to modern cybersecurity challenges.