Title: Enhanced Framework for a Software Piracy Protection System Using Classification Algorithm
Authors: Abubakar Balarabe, Murtala Muhammad Chafe, Ibrahim Abdullahi
Volume: 10
Issue: 2
Pages: 38-43
Publication Date: 2026/02/28
Abstract:
Software piracy remains a persistent global problem, particularly in developing countries where weak enforcement mechanisms, high software costs, and limited awareness contribute to widespread unauthorized software usage. Conventional software piracy protection techniques such as license keys, activation codes, and hardware-based mechanisms are increasingly ineffective due to cracking, reverse engineering, and key sharing. This paper proposes an enhanced framework for a software piracy protection system using a classification algorithm to intelligently detect and prevent unauthorized software usage. The proposed framework leverages machine learning-based classification techniques to analyze software usage behavior, system attributes, and licensing patterns in order to distinguish between legitimate and pirated software instances. The framework is composed of data collection, feature extraction, classification, decision enforcement, and monitoring modules, providing adaptability and scalability. Experimental evaluation demonstrates that the classification-based approach improves piracy detection accuracy and reduces false positives compared to traditional rule-based protection mechanisms. The study concludes that intelligent classification algorithms provide a more robust and sustainable solution for software piracy protection in modern software systems.