Ransomware Detector Using Artificial Neural Network

Ransomware Detector Using Artificial Neural Network

BHUKYA RAJA KUMAR¹,T GAYATHRI², P THOUHITH KHAN³,

M LAKSHMI VARAPRASAD⁴,  G SANJAY KRISHNA⁵

¹Assitant professor^{, 2,3,4,5} Students, Dept of CSIT

^1,2,3,4,5 Siddharth Institute of Engineering & Technology, Puttur-517583

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Abstract - This design demonstrates a new approach to detecting ransomware targeted at Microsoft Windows, combining 2 deep literacy neural network classifiers to produce an ensemble, taking lines as input in Microsoft’s standard PE train format, similar as those with a ‘. exe ’ train extension, and returning a vaticination of the train belonging to 1 of 3 classes benign, general malware, or ransomware. The model’s capability to distinguish between ransomware and other forms of malware allows it to be applied as an extension to a being malware discovery system similar as anti-virus software, and aid in the categorization and rear engineering of new in- the-wild ransomware samples. The results of testing the ensemble model on data not seen in its training suggest a high position of prophetic power in classifying new in- the-wild samples.

Keywords: Ransomware, Malware, Windows