Next-generation networks, including IoT, edge, vehicular, and 6G systems, require cryptographic schemes that not only resist attacks but also withstand channel degradation without disrupting secure data flows. This paper presents a novel adaptive encryption architecture for quantum-resilient networks that integrates real-time attack detection in quantum channels with per frame key management. A lightweight controller has been designed, which tracks the quantum bit error rate (QBER) via a sliding-window hysteresis and buffer thresholds, deciding per frame whether to derive symmetric keys from Quantum Key Distribution (QKD) or from a post-quantum cryptographic fallback, while maintaining a unified AES–GCM AEAD dataplane. Moreover, a deterministic HMAC-based key derivation function (HKDF) schedule binding eliminates key reuse across modes, retransmissions, and re-encapsulations. Experimental evaluation on image payloads shows: (i) timely QBER-spike detection with 9.5-frame latency and zero misses; (ii) full decryptability and tamper detection across both key sources; (iii) symmetric throughput of ≈ 1.1GB/s with negligible adaptation overhead; and (iv) near-ideal ciphertext metrics including entropy 8.02bits/pixel, NPCR ≈ 99.6%, and UACI ≈ 30.7%. The framework achieves secure, continuous encryption by preventing unsafe QKD use and maintaining authenticated operation under quantum-channel degradation.
Next-generation networks, including IoT, edge, vehicular, and 6G systems, require cryptographic schemes that not only resist attacks but also withstand channel degradation without disrupting secure data flows. This paper presents a novel adaptive encryption architecture for quantum-resilient networks that integrates real-time attack detection in quantum channels with per frame key management. A lightweight controller has been designed, which tracks the quantum bit error rate (QBER) via a sliding-window hysteresis and buffer thresholds, deciding per frame whether to derive symmetric keys from Quantum Key Distribution (QKD) or from a post-quantum cryptographic fallback, while maintaining a unified AES–GCM AEAD dataplane. Moreover, a deterministic HMAC-based key derivation function (HKDF) schedule binding eliminates key reuse across modes, retransmissions, and re-encapsulations. Experimental evaluation on image payloads shows: (i) timely QBER-spike detection with 9.5-frame latency and zero misses; (ii) full decryptability and tamper detection across both key sources; (iii) symmetric throughput of ≈ 1.1GB/s with negligible adaptation overhead; and (iv) near-ideal ciphertext metrics including entropy 8.02bits/pixel, NPCR ≈ 99.6%, and UACI ≈ 30.7%. The framework achieves secure, continuous encryption by preventing unsafe QKD use and maintaining authenticated operation under quantum-channel degradation.
In recent times, secure data transmission become crucial requirement because of increasing growth of threats and emergence of quantum computing technologies. However, existing Quantum Key Distribution-Advanced Encryption Standard-Least Significant Bit (QKD-AES-LSB) struggled with two main limitations such as restricted resistance to future quantum attacks because of dependence on classical encryption and vulnerability of static LSB steganography for improved Machine Learning (ML) approaches. To address these challenges, Quantum Key Distribution-Post-Quantum Cryptography-Adaptive Steganographic Encryption framework (QKD-PQC-ASE) is proposed. Primarily, secret data and cover images are gathered then quantum key is generated using E91 protocol. Next, post-quantum cryptographic key encapsulation is applied to strengthened the generated kay and ensure long-term quantum resistance. Further, secret data is encrypted employing quantum-resilient encryption mechanism and combined using cryptographic hashing. Furthermore, encrypted data is embedded in to cover image through quantum-randomness-guided adaptive steganography for resisting improved steganalysis attacks. Finally, proposed QKD-PQC-ASE framework obtains improved resistance against future quantum attacks and enhanced steganalysis by maintaining low computational overhead. Therefore, proposed QKD-PQC-ASE framework achieved greater results in terms of NPCR of 99.84 % and a UACI of57.92%which demonstrates efficiency of framework for secure and future-ready data encryption in quantum-aware communication environments.
The rapid integration of Internet of Things (IoT) technologies in Autonomous Vehicles (AVs) has significantly expanded the attack surface of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems.These networks exchange large volume of safety-critical data in realtime, which increases their exposure to attacks like message tampering, identity spoofing, and coordinated injection.Traditional cryptographic systems become more suspectable to emerging quantum computing threats, whereas the existing intrusion detection systems suffer from high latency, static encryption, and increased energy consumption due to frequent vehicular movement.These challenges restrict the ability of current systems to guarantee secure, real-time, energy-efficient, and sustainable communication for next-generation AVs.To address these challenges, this study introduced a novel Quantum-Enhanced Lightweight Protocol (QELP) that combines hybrid quantum-classical security mechanisms with adaptive encryption orchestration to improve security, real-time performance, and sustainable operation in IoT-enabled autonomous vehicle sensor networks.QELP combines BB84based Quantum Key Distribution (QKD) and lattice-based post-quantum cryptography (Kyber+) to establish quantum-resilient session keys.The context-aware encryption method dynamically adjusts the encryption intensity and key refresh rate based on residual energy, traffic density, anomaly score, and vehicular mobility to balance security strength and computational overhead.The model includes multi-layer error and attack detection techniques, including quantum-based intrusion alerts, digital signature validation, and behavioural deviation checks.The VeReMi and CARLA-NS3 datasets are employed for message-based V2X attack validation and to assess the networklevel behavior under real-time traffic scenarios.The performance is evaluated by simulations using Python (Qiskit) for quantum layers and NS3 for AV networks.The simulation results outperform with 98.76% attack detection rate, 0.015 false alarm rate, 13.7ms latency, 12.2% energy depletion score, 94.6% security risk score reduction towards a stronger quantumresilient defense and an encryption throughput improvement to 96.8%, handling encrypted data at high speed.The findings confirm that QELP provides a scalable, quantum-resilient, and energy-aware security architecture for next-generation autonomous vehicular communication systems.
The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.
The rapid proliferation of Internet of Things (IoT) devices presents critical cybersecurity challenges, as traditional cryptographic methods often fail to provide both robust security and operational efficiency in resource-constrained environments. This paper introduces Adaptive Quantum Cryptography (AQC), a novel framework designed to secure next-generation IoT networks against evolving classical and quantum-computing threats. The AQC framework synergistically integrates Quantum Key Distribution (QKD) for unbreakable key exchange, Quantum Random Number Generators (QRNGs) for high-entropy key generation, and Post-Quantum Cryptography (PQC) for quantum-resistant data encryption. A key feature of the system is its dynamic adaptation mechanism, which leverages artificial intelligence to intelligently select and adjust cryptographic protocols in real-time based on current network conditions, device computational capabilities, and identified threat levels. This ensures optimal security without imposing excessive overhead on IoT devices. Our comprehensive experimental evaluation demonstrates that the AQC framework significantly outperforms classical techniques, achieving a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum-based attacks. By providing forward secrecy and robust authentication, the proposed AQC framework offers a scalable, energy-efficient, and future-proof security solution, paving the way for resilient and trustworthy IoT ecosystems in the post-quantum era.
This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks.
Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience.