Quantum-Inspired Machine Learning Models for Secure Cryptographic Communication

Project Code :TCMAPY2425

Objective

This project focuses on developing a secure cryptographic communication framework using Quantum-Inspired Machine Learning techniques integrated with cybersecurity mechanisms. The system aims to detect malicious activities, unauthorized access attempts, and abnormal communication behaviors within network environments using intelligent machine learning models. Advanced algorithms such as Random Forest, XGBoost, Gradient Boosting, and ensemble-based approaches are explored to improve intrusion detection accuracy and secure data transmission. The project integrates data preprocessing, feature extraction, classification, and explainable AI techniques to analyze network traffic efficiently. The proposed framework enhances communication security, strengthens threat detection, and supports intelligent cyber defense systems for modern digital infrastructures and secure data exchange applications.

Abstract

The rapid growth of cyber threats and unauthorized network intrusions has increased the demand for intelligent and secure cryptographic communication systems. This project presents a web-based cybersecurity framework titled Quantum-Inspired Machine Learning Models for Secure Cryptographic Communication, which integrates advanced machine learning techniques with secure communication analysis to detect malicious network activities in real time. The proposed system is developed using Flask, MySQL, Pandas, NumPy, and Joblib, providing an interactive environment for user authentication, dataset uploading, model evaluation, and threat prediction. A Quantum-Inspired Random Forest (QRF) model is employed as the primary prediction engine to classify network traffic into multiple categories such as Benign, Botnet, BruteForce, DDoS, Infiltration, Malware, and PortScan attacks. The system processes important traffic features including flow duration, packet statistics, flow rate, and packet variance to identify abnormal communication behavior with high accuracy. In addition, the framework provides automated security recommendations based on the predicted threat category, enabling faster incident response and secure communication management. Comparative model analysis involving Quantum-Inspired XGBoost, CatBoost, Deep Learning, and Hybrid AI models demonstrates superior prediction performance with accuracy levels approaching 99.98%. The proposed framework enhances secure cryptographic communication by combining intelligent threat detection, scalable web technologies, and automated cybersecurity analysis, making it suitable for modern smart network infrastructures and enterprise-level security environments.

Keywords : Quantum-Inspired Machine Learning, Cryptographic Communication, Cybersecurity, Random Forest, DDoS Detection, Malware Detection, Intrusion Detection System, Flask Web Application, Network Security, Artificial Intelligence, Deep Learning, Secure Communication, Threat Classification, Quantum AI Models, Cyber Attack Prediction

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.