With the advancement of quantum computing, the long-term security of IoT systems is increasingly challenged, particularly due to the vulnerability of traditional public-key cryptography. This study integrates artificial intelligence (AI) with a hybrid strategy that involves Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) to address this challenge. Incoming traffic from the TON-IoT dataset is processed by AI using XGBoost, Random Forest, and One-Class SVM as anomaly-detection filters. Only traffic classified as normal is forwarded to the encryption phase. Postquantum key establishment is performed using Kyber-512, and the resulting shared secret is combined with simulated BB84based QKD key material via a key-derivation function (KDF) to derive a 256-bit session key, which is then used with AES-256GCM for authenticated IoT data encryption. Experimental results demonstrate that the hybrid algorithm, along with the AI, can effectively improve IoT security. Upon optimization of the parameter$\beta(=1.0)$, the Delivery Purity is 0.92, the Data Delivery rate is 0.80, and the Secure Delivery$\mathrm{F} \beta$score is 0.94 compared to Delivery Purity of 0.77, Delivery Rate of 1.0, and Secure Delivery Fβ score of 0.87, respectively, without AI.
With the advancement of quantum computing, the long-term security of IoT systems is increasingly challenged, particularly due to the vulnerability of traditional public-key cryptography. This study integrates artificial intelligence (AI) with a hybrid strategy that involves Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) to address this challenge. Incoming traffic from the TON-IoT dataset is processed by AI using XGBoost, Random Forest, and One-Class SVM as anomaly-detection filters. Only traffic classified as normal is forwarded to the encryption phase. Postquantum key establishment is performed using Kyber-512, and the resulting shared secret is combined with simulated BB84based QKD key material via a key-derivation function (KDF) to derive a 256-bit session key, which is then used with AES-256GCM for authenticated IoT data encryption. Experimental results demonstrate that the hybrid algorithm, along with the AI, can effectively improve IoT security. Upon optimization of the parameterβ(=1.0), the Delivery Purity is 0.92, the Data Delivery rate is 0.80, and the Secure DeliveryFβscore is 0.94 compared to Delivery Purity of 0.77, Delivery Rate of 1.0, and Secure Delivery Fβ score of 0.87, respectively, without AI.
Abstract The rapid expansion of Internet of Things (IoT) infrastructures has heightened their exposure to sophisticated adversaries, including those equipped with quantum capabilities. This work introduces a hybrid framework that integrates post-quantum cryptography (PQC), quantum key distribution (QKD), and quantum machine learning (QML) to deliver quantum-resilient anomaly detection. A novel PQC-IoT dataset is generated using real-time Raspberry Pi sensors, enriched with 30+ features spanning 12 traffic classes (9 attacks, 3 benign), and incorporating PQC-specific metadata such as encryption latency and ciphertext size. Security is enforced through Kyber-512 encryption and a BB84-based QKD channel, while anomaly detection is performed using a quantum support vector machine (qSVM). Experimental results show that qSVM consistently outperforms classical SVM and XGBoost by 4--7% in accuracy, precision, recall, and F1-score, achieving up to 97.6% accuracy with encryption overhead limited to 5--7%. The framework maintains a Quantum Bit Error Rate (QBER) below 11% under noise models, validating its robustness. To the best of our knowledge, this is the first reproducible benchmark that unifies PQC, QKD, and QML into a single pipeline, offering a practical pathway toward quantum-secure IoT anomaly detection.
The rapid advancement of quantum computing threatens the foundations of classical cryptography, creating severe risks for resource-constrained IoT systems widely used in healthcare, autonomous vehicles, and industrial automation. Conventional algorithms like RSA and ECC are vulnerable to Shor's and Grover's algorithms, exposing IoT to passive and active quantum-based attacks. This manuscript proposes a quantum-safe framework integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Artificial Intelligence (AI) to ensure confidentiality, integrity, and resilience. The framework leverages lattice, hash, and code-based PQC, lightweight cryptographic protocols, hybrid security models, and efficient key management to protect IoT communications. By combining PQC, QKD, and AI-driven adaptive security, the framework offers scalable, sustainable, and future-proof protection in the post-quantum era.
The imminent advent of quantum computing threatens the long-term security of classical public-key cryptography, creating critical challenges for resource-constrained internet of things (IoT) environments.Hybrid cryptographic frameworks combining elliptic curve cryptography (ECC) with post-quantum cryptography (PQC) primitives have emerged as a transitional solution; however, their security properties remain insufficiently validated under semi-trusted infrastructure assumptions.This paper presents a comprehensive security evaluation of a hybrid ECC-KEM authentication and key agreement framework targeting IoT deployments.Using a dual-verification methodology that integrates formal symbolic analysis with the Tamarin-Prover and practical AI-assisted testing via the APSVer tool, we identify a critical impersonation vulnerability that enables a semi-trusted third party (TTP) to reconstruct PUF-based authentication material.To mitigate this flaw, we introduce a protocol enhancement incorporating ML-DSA post-quantum digital signatures and device-isolated key derivation.Formal verification confirms that the enhanced protocol achieves mutual authentication, session key secrecy, replay resistance, and effective isolation of the TTP.Experimental performance analysis demonstrates that the proposed security improvements introduce only a 12%-15% computational overhead, preserving feasibility for constrained IoT devices.The proposed framework provides a practical, quantum-resilient authentication architecture suitable for next-generation secure IoT deployments.
The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.
Traditional encryption techniques face serious threats from the rapid development of quantum computing, which could soon compromise many classical algorithms. To address this, new quantum-resistant security methods are needed to protect private data and communications. This paper proposes a hybrid encryption approach that integrates quantum key distribution (QKD) with a simplified 3×3 advanced encryption standard (AES) key matrix, creating a lightweight system. The objective is to provide an efficient encryption method suitable for devices with limited processing power, such as IoT devices. The model was simulated using Qiskit, achieving an average quantum bit error rate of 0.00%, protocol efficiency of 49.72 %, and quantum fidelity of 1.000 under certain conditions. During eavesdropping scenarios, the error rate increased to 16.90 %. The simplified AES achieved a 50.14 % avalanche effect and an encryption time of 0.3073 ms, reducing latency by 17 % compared to standard AES. These results suggest that combining QKD with lightweight AES can provide quantum resilience and computational efficiency, offering a practical solution for secure communication in the post-quantum era.
Data, especially image data, is transmitted at an incredible rate due to the exponential growth in the number of connected devices brought about by the fast development of consumer electronics. Data transmission security remains fundamental in effective communication systems, especially with the emergence of the quantum era. Quantum advancements challenge traditional asymmetric cryptography, necessitating the requirement for new encryption schemes. The multimedia encryption schemes pose further challenges due to its large size, structural complexity, real-time requirements, and unique attack vectors specific to multimedia data. The current classical image encryption algorithms do not fully address the challenges posed by Quantum technology for securing the multimedia transmission. This paper proposes a hybrid framework combining Quantum Key Distribution (QKD), chaos-based encryption, and Post-Quantum Cryptography (PQC) to secure multimedia data, particularly images, in IoT consumer electronic networks. Integrating 2D-Logistic Sine Coupling Map (2D-LSCM), initialized with the Keys generated from QKD, and NTRU algorithm, this scheme achieves robust security and efficient performance. This new architecture improves processing speeds and enhances specificity and sensitivity values, fortifying chaotic map performance. Our experimental results and comprehensive security analysis demonstrate the security and performance of the framework. Correlation Analysis using Person correlation coefficient, with a nearing zero value, ensures the confidentiality of the encrypted image.