The rapid growth of the IoT has led to more cyberattacks on low-power devices. This study presents the IPQCLD model, which uses advanced security measures for future IoT networks to address these issues. It applies data preprocessing methods like Min-Max normalization and LDA, quantum key distribution using the BB84 protocol, and post-quantum cryptography techniques such as Lamport-Diffie one-time signatures. These features enhance authentication, encryption, and attack detection. The Lamport-Diffie method blocks key reuse and authenticates messages, while the BB84 protocol helps detect eavesdropping for secure key exchange. LDA and Min-Max normalization simplify data management and ensure accurate detection. Simulation results show that IPQCLD significantly outperforms baseline models, achieving a precision of 0.95 and a recall rate of 0.90.
The rapid growth of the IoT has led to more cyberattacks on low-power devices. This study presents the IPQCLD model, which uses advanced security measures for future IoT networks to address these issues. It applies data preprocessing methods like Min-Max normalization and LDA, quantum key distribution using the BB84 protocol, and post-quantum cryptography techniques such as Lamport-Diffie one-time signatures. These features enhance authentication, encryption, and attack detection. The Lamport-Diffie method blocks key reuse and authenticates messages, while the BB84 protocol helps detect eavesdropping for secure key exchange. LDA and Min-Max normalization simplify data management and ensure accurate detection. Simulation results show that IPQCLD significantly outperforms baseline models, achieving a precision of 0.95 and a recall rate of 0.90.
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.
The coupling of quantum-metering methods with cryptographic key exchanges spells out a new security paradigm model to safeguard Internet of Things (IoT) networks against rapidly changing cyberspace threats. The given assessment explains a quantum-advanced scenario that is operationally feasible and serves as a platform for quantum key distribution (QKD) along with classical post-quantum algorithms to deliver end-to-end confidentiality, integrity, and authentication features to a wide range of IoT devices. The model, by the virtue of quantum entanglement and photon polarization used in the creation of tamper-evident communication links, is invulnerable to adversarial-aided eavesdropping and computational assault methods and further offers a hybrid encryption protocol that has been demonstrated to alleviate key generation and exchange latency trade-offs - simultaneously maintaining scalability and effectiveness in resource-constrained IoT node environments. The key compromise probability is significantly lowered in the experimental results, along with the keys' entropy levels, which were generated in the pure QKD space as opposed to classicalbased RSA and ECC methods. Also, the framework investigates lightweight quantum-safe authentication techniques that can be used to establish trust at the device level. The output points to the enhanced resistance to quantum and classical attackers that can make the solutions work in real-time application IoT environments such as smart healthcare, autonomous systems, and industrial automation. Overall, the quantum-enriched model of cryptographic key exchange is next-gen IoT ecosystem to be implemented subsequently.
The explosion of constrained IoT devices with limited resources has increased the necessity of having efficient security measures to counter both the classical and new quantum threat. In this paper, we introduce Q-IoTGuard, a simple quantum cryptography platform that can be used in heterogeneous IoT networks. The model combines entanglement-assisted quantum key distribution (QKD) with lattice-based post-quantum encryption (PQC) in order to guarantee secure communication with a minimum of computational and energy overhead. The methodology commences with data preprocessing, in which the traffic of IoT and characteristics of the devices are organized, standardized and filtered in order to support the efficient cryptographic operations. Raw data is first fed into quantum-assisted key generation with simplified BB84 protocols with error correction and privacy amplification. Lightweight PQC encrypts then message providing resistance against quantum-enabled attacks. The simulation outcomes show that Q-IoTGuard has a low-key generation time, low latency, low-energy consumption, and high quantum attack resistance among sensors, actuators, and gateway devices. The flow-based architecture, which incorporates preprocessing, key gen, and lightweight encryption would make the framework practical to be deployed on resource-constrained IoT devices. Q-IoTGuard is a new scalable and energy-efficient system of securing next-generation IoT networks that addresses the gap between theoretical quantum security and the practical implementation.
The rapid proliferation of Internet of Things devices (IoT) across healthcare, smart homes, transportation, and industrial automation has led to an unprecedented exchange of sensitive data over wireless channels, exposing these networks to cyberattacks, eavesdropping, and unauthorized access.Conventional cryptographic techniques, which rely on computational hardness assumptions, are increasingly vulnerable to the emerging capabilities of quantum computing.This paper explores the application of Quantum Key Distribution (QKD) as a means of enabling secure and dependable data communication for IoT devices.By exploiting the fundamental principles of quantum mechanics, QKD allows two communicating parties to generate a shared secret key while inherently detecting any attempt at interception.The proposed approach aims to enhance data privacy, ensure secure authentication, preserve confidentiality, and safeguard IoT systems against present and future quantum enabled threats.The integration of quantum based security techniques into existing IoT infrastructure is examined, along with its implications for improving the reliability and trustworthiness of wireless IoT communication systems.This work positions Quantum Enhanced Secure Data Communication as a forward looking security solution for next generation IoT networks.
The resource limitations of IoT nodes and the vulnerability of wireless channels to cyberattacks and snooping, the quick spread of Internet of Things (IoT) devices in wireless surroundings has created serious challenges to data security and privacy. The development of quantum computing, which can meritoriously crack classical encryption, is posing a growing threat to recognized cryptographic techniques like RSA and ECC. By applying concepts from quantum mechanics like predicament cloning, quantum cryptography, in particular Quantum Key Distribution (QKD), provides tentatively unbreakable security. In order to integrate lightweight quantum key exchange with traditional IoT communication protocols, this paper proposes a comprehensive Quantum Cryptography Framework (QCF) exactly designed for Wireless IoT networks. The framework is made to preserve energy efficiency while certifying forward secrecy, confidentiality, integrity, and authentication. A performance evaluation in terms of latency, throughput, and resistance to quantum attacks supports our discussion of the architecture, working mechanism, and implementation deliberations. The findings show that, when compared to traditional encryption methods, the suggested QCF greatly improves the security of wireless IoT systems, certifying resilience against oppositional models of the future.
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.