INTELLIGENT APPROACHES TO SOURCE CODE PROTECTION

Abstract

The article considers an integrated source code protection technology that combines traditional obfuscation methods with the capabilities of artificial intelligence to optimize the protection process. A methodology based on the analysis of intermediate code (IL) in .NET applications is presented, where AI is used to automatically select and apply the most effective obfuscation strategies. The system is implemented using modern tools for working with IL code, such as Mono.Cecil, in combination with machine learning frameworks (.NET ML, TensorFlow.NET, and niches), which allows you to adapt the obfuscation process to the characteristics of a specific code. The methodology involves a phased analysis of the input code, where at the first stage syntactic and semantic analysis is performed to identify critical areas that require enhanced protection. The next stage is the application of an AI module, which, using recurrent neural networks (e.g., LSTM) and deep autoencoders combined into ensemble structures, allows predicting the optimal obfuscation strategy for each code segment. The integration of ensemble approaches allows combining predictions from several models, which significantly improves the accuracy and resistance of the system to reverse engineering.

The experiments conducted demonstrate that the integration of AI significantly increases the resistance of the code to reverse engineering, while maintaining the functionality of the software. The article considers the theoretical foundations, describes the architecture of the developed system, and demonstrates the results of experimental verification of the proposed approach, which confirm its effectiveness in modern software development conditions.