Ransomware Automatic Data Recognition Tool using SRANSAC
M. Manoj1, Rani V. G.2
1M. Manoj*, Assistant Professor, Department of Computer Science, Angappa College of Arts and Science, Seerapalayam, Coimbatore, India.
2Dr. RaniV. G., Associate Professor, Department of Computer Science Sri Ramakrishna College of Arts and Science for Women, Coimbatore, India.

Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2079-2083 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5778018520/2020©BEIESP | DOI: 10.35940/ijrte.E5778.018520

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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 Gand Crab. Research work also focuses in developing online storage process.
Keywords: OCR, SURF, Crysis, Gandcrab, Notpetya and Cryptojacking.
Scope of the Article: Pattern Recognition.