MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641125767 A) filed by Karpagam Academy Of Higher Education; Karpagam College Of Engineering; Dr. M. M. Karthikeyan; and Sharmila Devi R on October 22, 2026, for Ransomware Detection And Mitigation Using Ai-Powered Cyber Defense.
Inventors include Dr. M. M. Karthikeyan; and Sharmila Devi R.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: Ransomware has emerged as one of the most critical cybersecurity threats in modern digital environments, causing severe financial losses, data breaches, and operational disruptions across organizations worldwide. Traditional security mechanisms such as signature-based antivirus tools and rule-driven intrusion detection systems are increasingly inadequate against sophisticated ransomware attacks that employ obfuscation, encryption, and zero-day exploitation techniques. To address these limitations, this project presents an AI-powered cyber defense system for ransomware detection and mitigation, designed to provide proactive, adaptive, and real- time protection against evolving ransomware threats. The proposed system leverages artificial intelligence and machine learning techniques to continuously monitor system and network behavior, including file system activities, process execution patterns, memory utilization, and network traffic. By establishing baseline behavioral profiles of normal system operations, the system is capable of detecting anomalies that may indicate ransomware activity at an early stage. This behavior-based detection approach enables the identification of both known and unknown ransomware variants without relying solely on predefined malware signatures. Machine learning models are trained using a combination of benign and malicious activity datasets to enhance detection accuracy and minimize false positives. The models are designed to adapt dynamically by learning from newly observed patterns, allowing the system to remain effective against emerging ransomware techniques such as polymorphic and fileless attacks. Real-time anomaly detection ensures that potential threats are identified before large-scale data encryption or system compromise occurs. Upon detection of suspicious behavior, the system automatically initiates mitigation and response mechanisms without human intervention. These actions may include terminating malicious processes, isolating infected endpoints, blocking unauthorized network communications, securing critical files, and initiating data backup or restoration procedures. This automated response capability significantly reduces reaction time, prevents ransomware propagation, and minimizes data loss and system downtime. The proposed solution supports deployment across endpoint devices, enterprise networks, and cloud environments, making it suitable for diverse computing infrastructures. By correlating multiple indicators of compromise from different system layers, the system provides a comprehensive and resilient cybersecurity defense framework. The integration of real-time monitoring, intelligent threat analysis, and automated mitigation distinguishes the proposed approach from conventional ransomware protection systems. Overall, the AI-powered ransomware detection and mitigation system enhances cybersecurity resilience by offering proactive threat identification, adaptive learning, and rapid response capabilities. The proposed solution demonstrates strong potential for improving ransomware defense effectiveness and safeguarding critical digital assets in increasingly complex and hostile cyber environments.
Disclaimer: Curated by HT Syndication.
