Authors :
Bheemray; C.V. Vidyashri; Manasi. A.K.; M.V. Bharath; Ashwini M Rayannavar
Volume/Issue :
Volume 9 - 2024, Issue 11 - November
Google Scholar :
https://tinyurl.com/3rk52stf
Scribd :
https://tinyurl.com/3bjd33aa
DOI :
https://doi.org/10.5281/zenodo.14293158
Abstract :
Software piracy has become a major problem
for developers and companies as software development
keeps growing and changing. This paper provides a
thorough overview of machine learning methods used to
analyze installation metrics and user behavior patterns in
order to identify and prevent software piracy. The study
looks at how characteristics like usage hours, number of
installations, and licensing status might predict the
possibility of unlicensed consumption using algorithms
like Decision Trees, Support Vector Machines, and
Neural Networks. The survey assesses how well current
approaches detect unlicensed software usage, with a
particular emphasis on feature engineering, classification
strategies, and model evaluation metrics. The study also
highlights gaps in the literature, especially in areas like
real-time detection, adaptive models, and interaction with
software-as-a-service platforms, while identifying themes
that are frequently addressed, such classification
accuracy and user profiling. This initiative intends to
contribute to a better secure software ecosystem,
safeguard intellectual property, and offer insights into
improving pirate detection systems by investigating these
topics.
Keywords :
Software Piracy, Deep Learning Approach, Tensor Flow, Neural Network.
References :
- Sonal Bhattar, Mrunal Gaikwad, Pratibha Kasar, Yash Chikane, Pritam Ahire, “Software Piracy Detection using Deep Learning Approach”,International Journal of Engineering Research & Technology (IJERT) 02, February-2020
- Prof.Pritam Ahire, Mrunal Gaikwad, Pratibha Kasar, Sonal Bhatter, Yash Chikane, “Software Piracy Detection Using Deep Learning Approach”, International Journal of Creative Research Thoughts (IJCRT) , Volume 8, Issue 6 June 2020
- Chetan Pawar, Aniket Badekar, Kalyani Petkar, Asad Chaferkar, Nilesh Babar, Vikram Kadam, Professor. Shinde R.S, “Software Piracy Prevention”, International Research Journal of Engineering and Technology (IRJET), Volume: 03 Issue: 03 Mar-2016
- Kallol Bagchi, Peeter Kirs, Robert Cerveny, “Global Software Piracy”, Communications Of The Acm, June 2006
- Eric Kin-wai Lau, “Interaction effects in software piracy”, Business Ethics: A European Review, Volume 16 Number 1, January 2007
- Nicolas Dias Gomes, “Software Piracy: An Empirical Analysis”, Coimbra, 2014
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- Matthew Tooley, Thomas Belford “Detecting Video Piracy with Machine Learning”, NCTA, 30 Oct 2019
- Khalid Aldriwish, “A Deep Learning Approach for Malware and Software Piracy Threat Detection”, Vol. 11, No. 6, 2021
- Ruitao Feng, Jing Qiang Lim, Sen Chen, Shang-Wei Lin, and Yang Liu, “An Efficient Sequence-Based Malware Detection System Using RNN on Mobile Devices”, 10 Nov 2020
- ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb, “Android Malware Detection using Deep Learning on API Method Sequences”, Preprint submitted to Elsevier, 25 Dec 2017
- Matthew N. O. Sadiku, Mahamadou Tembely, and Sarhan M. Musa, “Software Piracy: A Primer”, International Journals of Advanced Research in Computer Science and Software Engineering, May 2018
- Zitian Liao, Shah Nazir, Anwar Hussain, Habib Ullah Khan , Muhammad Shafiq, “Software Piracy Awareness, Policy, and User Perspective in Educational Institutions”, Hindawi Scientific Programming, 27 November 2020
- Ishwor Khadka, “Software piracy: A study of causes, effects and preventive measures”, Helsinki Metropolia University of Applied Sciences, 14 January 2015
Software piracy has become a major problem
for developers and companies as software development
keeps growing and changing. This paper provides a
thorough overview of machine learning methods used to
analyze installation metrics and user behavior patterns in
order to identify and prevent software piracy. The study
looks at how characteristics like usage hours, number of
installations, and licensing status might predict the
possibility of unlicensed consumption using algorithms
like Decision Trees, Support Vector Machines, and
Neural Networks. The survey assesses how well current
approaches detect unlicensed software usage, with a
particular emphasis on feature engineering, classification
strategies, and model evaluation metrics. The study also
highlights gaps in the literature, especially in areas like
real-time detection, adaptive models, and interaction with
software-as-a-service platforms, while identifying themes
that are frequently addressed, such classification
accuracy and user profiling. This initiative intends to
contribute to a better secure software ecosystem,
safeguard intellectual property, and offer insights into
improving pirate detection systems by investigating these
topics.
Keywords :
Software Piracy, Deep Learning Approach, Tensor Flow, Neural Network.
