Abstract: Systems and methods for detecting tampering in software are disclosed. The system includes a preprocessor that converts source code into a minimal intermediate representation and extracts semantic and syntactic features using word embedding algorithms. The preprocessed data is then fed into two machine learning models: a classical LSTM model and a quantum LSTM model. The classical LSTM model detects basic tampering patterns, while the QLSTM model leverages quantum principles to enhance analysis and prediction of more complex tampering attempts. The system also includes a quantum cache for efficient data retrieval and manipulation, enabling real-time or near-real-time analysis. The combination of these features provides improved accuracy and effectiveness in detecting tampering, enabling timely intervention and mitigation of security threats. Remediation may be performed automatically or manually and can be based on historically determined or dynamically generated solutions.

Abstract: Systems and methods for detecting tampering in software are disclosed. The system includes a preprocessor that converts source code into a minimal intermediate representation and extracts semantic and syntactic features using word embedding algorithms. The preprocessed data is then fed into two machine learning models: a classical LSTM model and a quantum LSTM model. The classical LSTM model detects basic tampering patterns, while the QLSTM model leverages quantum principles to enhance analysis and prediction of more complex tampering attempts. The system also includes a quantum cache for efficient data retrieval and manipulation, enabling real-time or near-real-time analysis. The combination of these features provides improved accuracy and effectiveness in detecting tampering, enabling timely intervention and mitigation of security threats. Remediation may be performed automatically or manually and can be based on historically determined or dynamically generated solutions.