Neural Networks for White Box Cryptography in Software Protection Systems

This research explores the integration of neural networks into software protection mechanisms, focusing on enhancing cryptographic robustness against unlicensed copying and unauthorized access. The primary purpose is to develop a robust method that combines obfuscation and encryption to protect software by binding its activation and operation to specific hardware and user data, thereby preventing unlicensed replication and disassembly. The approach involves generating a unique hash of hardware identifiers, training a neural network on a remote server to produce a bytecode sequence for a virtual machine, and using the network’s weights as an activation code. Key results show that optimal neural network configurations, particularly those with two dense layers and one LSTM layer, achieve 100% accuracy in mapping predefined inputs to specific bytecodes within an average training time of 11 seconds, while generating pseudorandom outputs for all other inputs. Statistical analysis of output distributions reveals high entropy, demonstrating resilience against statistical attacks. The proposed method offers a layered defense against common attack vectors and ensures persistent security throughout the software’s lifecycle.