Ransomware Automatic Data Recognition Tool using SRANSAC

Abstract

In recent times, Ransomware is the most common form of malware seen which are achieved through ransomware attacks. The most common attacks are DDoS, Malicious Insiders, and Phishing. In this research work, information related to the ransomware attacks on windows and Linux are extracted, the detection of OCR(Optical character recognition) is improved to generate the screenshot of the infected machine and corresponding information are added to the database so that patterns are enhanced. The Hybrid Speeded Up Robust Feature (SURF) algorithm and image matching using Random Sample Consensus (SRANSAC) algorithm, bundle adjustment and image blending algorithms are used to develop the proposed model. An additional step is taken to crop the dark surrounding areas in the stitched image. Frequently used ransomware are crysis,gandcrab, crypto jacking and Notpetya. If the ransomware attack is detected in online data then the stored results is implemented so that USB dependence is avoided and to safeguard from the Ransomware like Crysis or GandCrab. Research work also focuses in developing online storage process.