Quantum Resistant AI Powered Cyber Security in Python Projects

Quantum Resistant AI Powered Cyber Security in Python Projects

Abstract

The advent of quantum computing poses new threats to traditional cryptographic systems, making cybersecurity more vulnerable to advanced attacks. The project Quantum Resistant AI-Powered Cybersecurity in Python Projects focuses on developing an intelligent security framework that leverages artificial intelligence and quantum-resistant cryptographic techniques to protect digital infrastructures. Python is chosen as the development platform due to its extensive libraries for machine learning, cryptography, and data analysis, including TensorFlow, Keras, Scikit-learn, and PyCryptodome. The system integrates AI models to detect anomalies, potential intrusions, and malware, while employing quantum-resistant encryption algorithms such as lattice-based or hash-based cryptography to secure data transmission. This approach ensures robust defense mechanisms against both classical and quantum-enabled cyber attacks, enhancing the resilience and integrity of information systems.

Existing System

Current cybersecurity frameworks primarily rely on classical encryption techniques, signature-based intrusion detection systems, and traditional machine learning approaches for threat detection. While effective against conventional cyber threats, these systems are vulnerable to attacks from quantum computers, which can break widely-used encryption algorithms such as RSA and ECC. Existing AI-based security systems improve threat detection but do not incorporate quantum-resistant cryptography, leaving sensitive data exposed to future quantum attacks. Additionally, many systems lack the capability to dynamically adapt to emerging threats using AI-driven analytics, reducing their effectiveness in proactive cyber defense.

Proposed System

The proposed system introduces a Python-based AI-powered cybersecurity framework integrated with quantum-resistant encryption techniques. Network traffic, user behavior, and system logs are collected and preprocessed for anomaly detection. AI models, including deep learning architectures such as CNN, LSTM, or autoencoders, are trained to identify malicious patterns, intrusions, and abnormal activities in real time. Simultaneously, sensitive data is encrypted using quantum-resistant algorithms like lattice-based, multivariate, or hash-based cryptography to protect against quantum-enabled attacks. The system evaluates performance using metrics such as detection accuracy, precision, recall, F1-score, and encryption efficiency. By combining AI-driven threat detection with quantum-resistant cryptography, this system provides a scalable, proactive, and secure solution for modern cybersecurity, safeguarding digital assets against both current and future cyber threats.