IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Scaling Up Software Birthmarks Using Fuzzy Hashing

Scaling Up Software Birthmarks Using Fuzzy Hashing
View Sample PDF
Author(s): Takehiro Tsuzaki (Graduate School of Kyoto Sangyo University, Kyoto, Japan), Teruaki Yamamoto (Kyoto Sangyo University, Kyoto, Japan), Haruaki Tamada (Kyoto Sangyo University, Kyoto, Japan)and Akito Monden (Okayama University, Okayama, Japan)
Copyright: 2021
Pages: 16
Source title: Research Anthology on Recent Trends, Tools, and Implications of Computer Programming
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-3016-0.ch039

Purchase

View Scaling Up Software Birthmarks Using Fuzzy Hashing on the publisher's website for pricing and purchasing information.

Abstract

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.

Related Content

Preethi, Sapna R., Mohammed Mujeer Ulla. © 2023. 16 pages.
Srividya P.. © 2023. 12 pages.
Preeti Sahu. © 2023. 15 pages.
Vandana Niranjan. © 2023. 23 pages.
S. Darwin, E. Fantin Irudaya Raj, M. Appadurai, M. Chithambara Thanu. © 2023. 33 pages.
Shankara Murthy H. M., Niranjana Rai, Ramakrishna N. Hegde. © 2023. 23 pages.
Jothimani K., Bhagya Jyothi K. L.. © 2023. 19 pages.
Body Bottom