Ransomware Defence Systems

Abstract

Ransomware has quickly become one of the newest and most harmful types of cyberattacks. Traditional security programs like antivirus programs, firewalls and signature-based Endpoint Detection and Response (EDR) programs are no longer good enough, particularly in dealing with the zero-day attacks and the new types of ransomwares that evolve their behavior to evade detection. This creates a major threat to organizations, such as operational disruptions, loss of money and irreparable data breach. To overcome these shortcomings, the Hybrid Ransomware Defense Framework (HRDF) is presented in this report, a multi-layered and dynamic security framework that will provide comprehensive protection at every phase of a ransomware attack. HRDF combines AI-based anomaly detection, deception-based honeypot validation, Zero Trust security policy, and automated responses in a single, unified defense platform. Its detection layer uses a dual-model machine learning approach, which incorporates both Random Forest classification and LSTM-based behavioral analysis as its means of detection, which allows the system to detect both structural anomalies and time-based attack patterns in real time. The automation that is inherent in HRDF significantly lowers the Mean Time to Detect (MTTD) as well as the Mean Time to Respond (MTTR), hence quick containment and recovery. In general, HRDF offers end-to-end ransomware protection that is beyond conventional approaches, and it includes proactive detection, trusted prevention, and automated recovery that is smooth enough to fit today’s advanced threat environment.