Software Piracy Detection using Deep Learning Approach

Abstract

Software piracy can be referred as illegally stealing citations. Currently, every other installed software is pirated. There are many scenarios of this happening, the attacker may crack the original legal software and re-construct or re-design the logic into other programming language or may change minor details of the software. It is very exasperating to catch such assaulters malicious activities as all the programming language have their own syntax and semantic structures. Currently, software piracy is high risk for security of software. It may cause reputational and economic damages. Now a days every other software is pirated there are many scenarios in which it can occur, the programmer may crack the original legal software and reconstruct or re-design the logic into other programming languages or may change the minor details of the software so we proposed a combine Deep learning approach to detect the pirated software. The Tensor Flow deep neural network is proposed to identify pirated the techniques like Tokenization and weighting are used to filter noisy data. The dataset is collected from Google code Jam (GCJ) to find the software piracy. The process of software piracy is very exasperating to each such assaulter malicious activities as all the programming languages have their own syntax and semantic structure. The experiment result shows that how much percentage of software code is plagiarism which be effective from current available methods. Keywords— Deep Learning, Machine Learning, Tensor Flow, Piracy, Neural Network, Plagiarism, TF-IDF.