Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An algorithm for detecting an attacker between a transmitter and receiver is proposed, with the side effect of interrupting the QKD process while detecting the attacker. Afterwards, Artificial neural network (ANN) and deep learning (DL) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers. In addition, an implementation scenario for securing IoT communications for sensors deployed in railroad networks is described. The results show that the proposed ML techniques can reach 99% accuracy in detecting attackers.
Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An algorithm for detecting an attacker between a transmitter and receiver is proposed, with the side effect of interrupting the QKD process while detecting the attacker. Afterwards, Artificial neural network (ANN) and deep learning (DL) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers. In addition, an implementation scenario for securing IoT communications for sensors deployed in railroad networks is described. The results show that the proposed ML techniques can reach 99% accuracy in detecting attackers.
Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers, while considering the use case of sensors deployed in railroad networks. Neural Network (NN) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. The results show that the proposed techniques can reach 99% accuracy.
In quantum communication era, where quantum computing capabilities can be used in cyber attacks, it is important that Internet of Things (IoT) devices, which have limited energy and computing power compared to central devices, can communicate securely with control devices. In our study, we performed a simulation based on two scenarios for quantum key distribution (QKD) with the absence and presence of attack between server and IoT device. Security of communication for IoT device is achieved by detecting the attacks on QKD process with a machine learning (ML) algorithm. Unlike traditional cyber security methods, the ML algorithm helped to detect attacks with 100% probability without interrupting the flow of information between the server and the IoT device. The proposed simulation can also be generalised for being suitable for other applications where quantum communications are used.
The rapid implementation of the Internet of Things (IoT) has raised security concerns, therefore revealing significant vulnerabilities in many sectors, including smart cities, healthcare, and agriculture. This extensive analysis of quantum computing and IoT security devices delineates the advantages of quantum algorithms and the potential for securing communication. Protocols including quantum concepts such as superposition, entanglement, and quantum interference, leading to safe key distribution and authentication procedures, are examined. This paper introduces a novel strategy to reduce the risks that quantum computing presents to IoT systems, which current cryptography solutions fail to adequately address. Quantum computers may use these vulnerabilities to undermine key-pair formation and get private keys from transaction signatures. These transaction signatures are optimized for low-power, cost-effective microcontrollers, such as the ESP32, making the solution accessible for a diverse array of IoT devices. The report features a case study on a post-quantum secure portable device for detecting blood oxygen levels and heart rate, demonstrating the practical advantages and efficacy of the suggested method in improving IoT security against quantum threats. The review addresses quantum key distribution (QKD) protocols, quantum authentication methods proposed as solutions, and the difficulties associated with implementing quantum cryptography techniques in IoT systems. This review assesses the feasibility of quantum-enabled communication, explores its applications across several industries, and examines existing quantum software tools. The objective is to provide a basis for future research and effective practical solutions in the dynamic realm of IoT security.
In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol.
Quantum Key Distribution (QKD) is an auspicious model for Internet of Things (IoT) networks against attacks of eavesdropping. Nevertheless, standard quantum-based techniques are expensive and overweighted for resource-awkward devices in IoT. Especially, an out-band quantum channel needs to use for regular exchanges among the devices and QKD controller. To decreased IoT devices overweight pain against eavesdropping, implemented a new Quantum-based Secure and Lightweight Transmission (QSLT) method in this work. In particular, the system pre-distributes quantum keys to IoT SIM cards-equipped devices. Sensitive IoT data can be encrypted or decrypted using one of the keys by QSLT. It is important that two distinct devices arranged the session key using key-selection of in-band techniques. Once the data is received by another device, the key-selection field is extracts by QSLT and uses locally stored selected quantum key to decrypt the data. Investigated the implemented technique and analyses its performances of transmission and security.
The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining quantum computing (QC) with machine learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. QC, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, QC, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating QC approaches with ML. It also addresses the challenges and limitations of integrating ML and QC in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.