Software Birthmark Usability for Source Code Transformation Using Machine Learning Algorithms

Source code transformation is a way in which source code of a program is transformed by observing any operation for generating another or nearly the same program. This is mostly performed in situations of piracy where the pirates want the ownership of the software program. Various approaches are being practiced for source code transformation and code obfuscation. Researchers tried to overcome the issue of modifying the source code and prevent it from the people who want to change the source code. Among the existing approaches, software birthmark was one of the approaches developed with the aim to detect software piracy that exists in the software. Various features are extracted from software which are collectively termed as “software birthmark.” Based on these extracted features, the piracy that exists in the software can be detected. Birthmarks are considered to insist on the source code and executable of certain programming languages. The usability of software birthmark can protect software by any modification or changes and ultimately preserve the ownership of software. The proposed study has used machine learning algorithms for classification of the usability of existing software birthmarks in terms of source code transformation. The K-nearest neighbors (K-NN) algorithm was used for classification of the software birthmarks. For cross-validation, the algorithms of decision rules, decomposition tree, and LTF-C were used. The experimental results show the effectiveness of the proposed research.

Scaling Up Software Birthmarks Using Fuzzy Hashing

To detect the software theft, software birthmarks have been proposed. Software birthmark systems extract software birthmarks, which are native characteristics of software, from binary programs, and compare them by computing the similarity between birthmarks. This paper proposes a new procedure for scaling up the birthmark systems. While conventional birthmark systems are composed of the birthmark extraction phase and the birthmark comparison phase, the proposed method adds two new phases between extraction and comparison, namely, compression phase, which employs fuzzy hashing, and pre-comparison phase, which aims to increase distinction property of birthmarks. The proposed method enables us to reduce the required time in the comparison phase, so that it can be applied to detect software theft among many larger scale software products. From an experimental evaluation, the authors found that the proposed method significantly reduces the comparison time, and keeps the distinction performance, which is one of the important properties of the birthmark. Also, the preservation performance is acceptable when the threshold value is properly set.

Modelling Features-Based Birthmarks for Security of End-to-End Communication System

Feature-based software birthmark is an essential property of software that can be used for the detection of software theft and many other purposes like to assess the security in end-to-end communication systems. Research on feature-based software birthmark shows that using the feature-based software birthmark joint with the practice of software birthmark estimation together can deliver a right and influential method for detecting software piracy and the amount of piracy done by a software. This can also guide developers in improving security of end-to-end communication system. Modern day software industry and systems are in demand to have an unbiased method for comparing the features-based birthmark of software competently, and more concretely for the detecting software piracy and assessing the security of end-to-end communication systems. In this paper, we proposed a mathematical model, which is based on a differential system, to present feature-based software birthmark. The model presented in this paper provides an exclusive way for the features-based birthmark of software and then can be used for comparing birthmark and assessing security of end-to-end communication systems. The results of this method show that the proposed model is efficient in terms of effectiveness and correctness for the features-based software birthmark comparison and security assessment purposes.

Scaling Up Software Birthmarks Using Fuzzy Hashing

To detect the software theft, software birthmarks have been proposed. Software birthmark systems extract software birthmarks, which are native characteristics of software, from binary programs, and compare them by computing the similarity between birthmarks. This paper proposes a new procedure for scaling up the birthmark systems. While conventional birthmark systems are composed of the birthmark extraction phase and the birthmark comparison phase, the proposed method adds two new phases between extraction and comparison, namely, compression phase, which employs fuzzy hashing, and pre-comparison phase, which aims to increase distinction property of birthmarks. The proposed method enables us to reduce the required time in the comparison phase, so that it can be applied to detect software theft among many larger scale software products. From an experimental evaluation, the authors found that the proposed method significantly reduces the comparison time, and keeps the distinction performance, which is one of the important properties of the birthmark. Also, the preservation performance is acceptable when the threshold value is properly set.