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@article{186772,
        author = {Miss.Sonas Gauri U. and Miss.Thorat Bhakti D. and Miss.Hon Sanika S. and Miss.Kolhe Rutuja S. and Prof. Gulhane V.M. and Mr. Abhale B.A.},
        title = {Post-Quantum Cryptography Enhanced with Machine Learning for Intelligent Cyber Threat Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1938-1943},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186772},
        abstract = {With the rapid advancement of quantum computing, traditional cryptographic algorithms such as RSA and ECC are becoming vulnerable to quantum attacks. Post-Quantum Cryptography (PQC) has emerged as a promising solution to ensure data security in the quantum era. However, while PQC strengthens encryption mechanisms, modern cyber threats are increasingly intelligent, adaptive, and capable of exploiting system vulnerabilities beyond encryption layers. To address this dual challenge, this study proposes an integrated framework combining PQC and Machine Learning (ML) for intelligent cyber threat detection. The PQC component ensures resilience against quantum-based attacks, while ML algorithms enhance system intelligence by identifying anomalous behaviors, zero-day threats, and advanced persistent attacks in real time.},
        keywords = {Artificial Intelligence (AI); Anomaly Detection; Cybersecurity; Cyber Threat Detection; Data Protection; Deep Learning; Machine Learning (ML); Network Security; Post-Quantum Cryptography (PQC); Quantum Computing; Quantum-Resilient Encryption; Supervised Learning; Unsupervised Learning; Zero-Day Attack Detection; Intelligent Cyber Defence; Quantum-Safe Cryptographic Algorithms.},
        month = {November},
        }