Lightweight Post Quantum Homomorphic Encryption for Secure Network Traffic Intrusion detection
These quantum computing technologies are a serious risk for existing “Cryptographic Algorithms, corresponding “RSA, ECC, and Diffie-Hellman”. The Quantum Algorithm “Shor's can break classical” public-key encryption systems effectively and now there is a momentous security concern of cloud computing, IoT systems, and healthcare networks, as well as the future 6G communication systems. In response to the above difficulties, researchers have suggested two new methods of privacy-preserving encrypted computation, namely: “Post-Quantum Cryptography (PQC) and Fully Homomorphic Encryption (FHE)". But the current “Post-Quantum Homomorphic Encryption (PQHE)” solutions have high computational complexity, expanded ciphertext size, latency, and are not widely deployed in cybersecurity applications. This research aims to provide a lightweight Post-Quantum Homomorphic Encryption framework for secure network traffic analysis with the intrusion detection dataset from CICIDs 2018. The proposed framework combines the lattice-based PQHE concepts, “Principal Component Analysis (PCA) and K-Means cluster to envision encrypted traffic and secure intrusion analysis. Data preprocessing, feature standardization, dimensionality reduction, encrypted traffic representation, clustering analysis and graphical visualization are parts of the experimental methodology. To address the key challenges, by using two major approaches for privacy-preserving encrypted computation have been proposed: “Post-Quantum Cryptography (PQC) and Fully Homomorphic Encryption (FHE)”. The current Post-Quantum Homomorphic Encryption (PQHE) systems, on the contrary, are complicated, costly in the length of the ciphertext, slow and not widely used in terms of cybersecurity. To protect network traffic analysis a lightweight Post-Quantum Homomorphic Encryption framework is proposed in this research to achieve network security using intrusion detection data set CICIDS2018.