Graphs Resemblance based Software Birthmarks through Data Mining for Piracy Control
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raphs Resemblance based Software Birthmarks through Data Mining for Piracy Control S. Sarwara,*, Z. Ul. Qayyuma,**, M. Safyanb,***, M. Iqbalc,d,****, and Y. Mahmooda,***** aDepartment
of Computer Science, University of Gujrat Jalalpur Jattan Road, Gujrat, Punjab, 50700 Pakistan b Department of Computing, GC University Lahore Katchery Rd, Anarkali Bazaar, Lahore, Punjab, 54000 Pakistan c School of Engineering, London South Bank University 103 Borough Rd, London, SE1 0AA England dSchool of Computer Science and Electronic Engineering University of Essex Wivenhoe Park, University of Essex, Colchester, CO4 3SQ England *e-mail: [email protected] **e-mail: [email protected] ***e-mail: [email protected] ****e-mail: [email protected] *****e-mail: [email protected] Received September 18, 2019; revised October 17, 2019; accepted October 25, 2019
Abstract—The emergence of software artifacts greatly emphasizes the need for protecting intellectual property rights (IPR) hampered by software piracy requiring effective measures for software piracy control. Software birthmarking targets to counter ownership theft of software by identifying similarity of their origins. A novice birthmarking approach has been proposed in this paper that is based on hybrid of text-mining and graph-mining techniques. The code elements of a program and their relations with other elements have been identified through their properties (i.e., code constructs) and transformed into Graph Manipulation Language (GML). The software birthmarks generated by exploiting the graph theoretic properties (through clustering coefficient) are used for the classifications of similarity or dissimilarity of two programs. The proposed technique has been evaluated over metrics of credibility, resilience, method theft, modified code detection and selfcopy detection for programs asserting the effectiveness of proposed approach against software ownership theft. The comparative analysis of proposed approach with contemporary ones shows better results for having properties and relations of program nodes and for employing dynamic techniques of graph mining without adding any overhead (such as increased program size and processing cost). DOI: 10.1134/S0361768819080152
1. INTRODUCTION The revolutionary impact of software engineering has changed the course of technological advancements through innovative ideas. Implementation of these ideas has entailed in a paradigm shift with concepts like mobile apps, smart technologies (smart homes, offices and cities etc), computational paradigms (grid and cloud computing) and Technology enhanced Learning (TeL) and Internet of Things (IoT) etc. These innovative software developments based on novelty of ideas (i.e. intellectual property), are facing potential threats. Few of these threats to intellectual property rights (IPR) are software piracy, ownership theft, reverse engineering and software copying (or copying of software parts) etc. One of the
studies asserts that more than 50% of technology consumers are working on pirated software [
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