Software Piracy Detection using Deep Learning

Inspiration

In today’s world software piracy is high risk to compromise the security in computer world. The detection of software piracy is the main aim in the field of cyber security. In proposed system, a combined deep learning approach is proposed to identify and detect pirated software.

What it does

Proposed system will help to avoid the reputational and economical damages to the software industry. The traditional methods available may solve the concern but high computational cost will be needed to do so.

How we built it

This involves two steps: First is preprocessing of the code collected from the GCJ (Google Code Jam)which breaks code into small pieces. And the second step is identification of software piracy using plagiarism which uses Tensor Flow Neural Network.

Challenges we ran into

The detection of software piracy and malware threats are the main challenges in the field of cyber security using IoT-based big data. This system proposed a combined deep learning based approach for the identification of pirated and malware files. First, the Tensor Flow neural network is proposed to detect the pirated feature of original software using software plagiarism. We collected 100 programmer’s source code files from CGJ to investigate the proposed approach. The source code is preprocessed to clean the noise and to capture further the high-quality features which include useful tokens. Then, TFIDF and LogTF weighting techniques are used to zoom the token in terms of source code similarity.

Accomplishments that we're proud of

we proposed a novel methodology based on convolution neural network and color image visualization to detect malware using IoT. We have converted the malware files into color images to get better malware visualized features. Then, system passed these visualized features of malware into deep convolution neural network. The experimental results show that the combined approaches retrieve maximum classification results as compared to the state of the art techniques.

What we learned

In this section we discuss about the overview of our proposed system, the advancements of the proposed system over existing system, overview of the tool we have chosen for system implementation and in depth understanding of the architecture of our proposed system with the initial implementation that we have done.

What's next for Software Piracy Detection using Deep Learning

The in-depth learning approach is designed to identify similar source codes in different types of programming languages using Tensor Flow framework. Then, the extracted similar codes are used to identify the pirated software.