Author(s): Mayank Parashar, Tanvi Dhingra, Rajat Nagar, Pawan Kant Tiwari and Deepika Singh

Abstract: This paper “malware detection using machine learning with cloud support” aims to present the functionality and accuracy of five different machine learning algorithms to detect whether an executable is infested or clean. It starts by prompting the user to login into the system by entering valid credentials. Upon successful validation of the details, the user is asked to upload the binary that he/she wants to store in the cloud database. After this, when the user uploads a file from his system, the underlying machine learning algorithms would analyse that the file is benign or infested by means of various classification algorithms. The output provided by the algorithm with highest accuracy is considered and the corresponding result is displayed to the user. The user thus becomes aware if the file uploaded by him/her contains malware or not. In case the file is found to be containing malware, it is discarded, and on the contrary if it is found to be legitimate it is stored on the Cloud by making use of Amazon S3(Simple Storage Service) and thus can be accessed by the user anytime in future. This is how malware is detected successfully by making use of Web, Machine Learning and Cloud Services.

DOI: 10.33545/26633582.2021.v3.i2a.55

Pages: 22-26 | Views: 855 | Downloads: 382

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