Jul 2026· International Conference Computing Methodologies and Communication· pp. 534-538· 0 citations· 19 references
Abstract
Their combination of adversarial AI attacks with cryptographically relevant quantum computers endangers the traditional public-key infrastructure. This paper proposes a hybrid system that combines real-time deep learning-based threat detection and Quantum Key Distribution (QKD) to secure communication systems. A convolutional neural network with attention mechanism detects network anomalies with 98.4% accuracy on the CIC-IDS-2017 dataset. At the same time, a decoy-state BB84 QKD protocol is used to create symmetric keys on a modeled 40 km fiber channel with a quantum bit error rate of less than 2.5%. A new query fragment caching algorithm cuts the key delivery latency by a factor of 37. Empirical evidence demonstrates the system throughput of 850 Mbps and key generation rate of 12.4 kbps providing a viable roadmap to information-theoretically secure communication when using active cyber threats.
The escalation of security attacks has led to a growing complexity and posed serious challenges for traditional IDS paradigms to securing and sustaining a resilient network infrastructure. Current solutions are generally inefficient at optimizing detection parameters, cannot detect attacks that have previously not been encountered and do not offer clear decision making processes. In this work, a framework for optimization of secure data protection using quantum computing is proposed which combines machine learning, anomaly detection, explainable AI and quantum computing inspired optimization techniques in a single cybersecurity framework to overcome these limitations. Selected network traffic from CICIDS2017 data set is used in the system to detect various types of attacks such as Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), PortScan and WebAttack. The multi-class intrusion detection uses an XGBoost (Extreme Gradient Boosted) Classifier to learn discriminative patterns from network flow features. The Quantum Approximate Optimization Algorithm (QAOA) is integrated to optimize certain model parameters (learning rate, tree depth and decision thresholds) that can enhance detection performance. Moreover, an Isolation Forest model is run concurrently to detect zero day and unknown anomalies not found in the training set. To enhance the interpretability, SHAP-based explainability is incorporated to measure the network's contribution to each prediction by its features. All the framework is deployed via Flask and is displayed on an interactive dashboard that shows attack classifications, threat risk scores, optimized configurations and explanatory insights. The hybrid architecture proposed shows a novel combination of classical artificial intelligence and quantum optimization techniques to create an adaptive, explainable, and intelligent cybersecurity solution.
M. Anusha, M. Neha, PG Student et al.· 2026 International Conferenc...· 0 citations
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.
Mariam Nayab, Muhammad Sajid Qureshi, Abdul Jabbar· International Journal of Art...· 0 citations
The proposed framework can improve threat detection and system resilience in critical infrastructure contexts and support the use of kernel-based quantum-inspired representations as a tunable early-warning layer for PSAP traffic monitoring, while also showing that threshold calibration and operational context remain necessary before deployment.
Carlos B. Rosa-Remedios, P. Caballero-Gil, J. Molina-Gil· Computers, Materials & C...· 0 citations
Vehicular Ad Hoc Networks (VANETs) are a fundamental technology for intelligent transportation systems, enabling real-time communication between vehicles, roadside infrastructure, and cloud-based services. However, the increasing connectivity of vehicles introduces significant cybersecurity challenges, including replay attacks, Sybil attacks, false message injection, and denial-of-service attacks. Existing VANET security mechanisms primarily rely on conventional cryptographic algorithms such as RSA and ECC, which are vulnerable to future quantum computing threats. Furthermore, traditional intrusion detection systems lack the intelligence and adaptability required to detect evolving cyber threats in highly dynamic vehicular environments.
This paper proposes a Hybrid Post-Quantum Cryptography and Machine Learning Framework for Intrusion Detection in VANETs (HPQC-ML-VANET). The proposed framework integrates post-quantum cryptographic mechanisms based on lattice-based algorithms with a machine learning-driven intrusion detection system. The cryptographic layer provides quantum-resistant authentication and secure key exchange, while the machine learning layer performs behavioural analysis and real-time anomaly detection. The proposed approach combines CRYSTALS-Kyber for secure key encapsulation and CRYSTALS-Di lithium for digital authentication with a hybrid machine learning model based on feature extraction, dimensionality reduction, and ensemble classification.
The framework is evaluated using simulated VANET communication environments incorporating realistic vehicle mobility and cyberattack scenarios. Performance evaluation considers detection accuracy, false positive rate, communication overhead, and detection latency. The proposed framework aims to provide a scalable, privacy-preserving, and quantum-resistant security solution for future autonomous and connected vehicles.
Moses O. . Bankole· Global Journal of Engineerin...· 0 citations
This systematic review critically examines hybrid models of quantum and classical artificial intelligence, focusing on architectures for quantum key distribution, intrusion detection, network management, and the integration of post-quantum cryptography, concluding that current evidence supports application-specific feasibility rather than universal quantum advantage.
Kyiewu Bernard, A. Clinton, Odoi Henry et al.· Journal of Electrical System...· 0 citations
In the rapidly evolving landscape of cybersecurity, traditional cryptographic systems are increasingly vulnerable to attacks, including brute-force, side-channel, man-in-the-middle, replay, and ransomware attacks, highlight the limitations of classical encryption techniques. Quantum cryptography leverages the no-cloning theorem and the properties of quantum states to establish fundamentally secure communication protocols with intrinsic eavesdropping detection capabilities. This security framework provides information-theoretic protection beyond the mathematical assumptions underlying conventional cryptographic systems. Quantum image security has evolved into two major paradigms: Quantum Key Distribution (QKD) and Quantum Secure Direct Communication (QSDC). Although recent surveys have reviewed both approaches chronologically, they have not systematically analysed their security thresholds The objective of this paper proposes a three-axis taxonomy of QSDC protocols, classifying them by quantum resource type, physical transmission channel, and device trust model. Furthermore, it presents a comparative performance analysis of QKD employing a hyperchaotic cipher over QSDC channels across seven quantum image representations, including FRQI, NEQR, GQIR, and MCQI. The analysis shows that QKD-seeded schemes achieve efficient key distribution, whereas pixel-level security remains dependent on cipher complexity. In contrast, QSDC provides end-to-end security governed by quantum mechanical principles; hyperentangled carriers achieve an eavesdropping detection probability of 0.875 compared with 0.5 for conventional two-step protocols, although communication throughput remains a limiting factor. Based on these findings, this review outlines future research directions, including QSDC-specific quantum repeaters for continental-scale deployment, hyperentangled carriers supporting up to 12 bits per photon pair compatible with NEQR’s 8-bit encoding, and machine-learning-assisted management of hybrid fiber–free-space quantum communication networks.
S. Deepika, N. Jeyanthi· Frontiers of Physics· 0 citations
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