Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

There has been a dramatic increase in the number of Internet of Things (IoT) devices and their applications. Furthermore, there is a growing impetus to integrate IoT networks on a global scale, using satellites to expand the range of IoT connectivity into geographically remote areas. Ensuring the security of satellite backhaul for IoT networks is thus of paramount importance. The steady advance of quantum computing in recent years threatens to nullify classical cryptographic approaches based on assumptions of computational hardness, motivating the need for post-quantum cryptography. Quantum computing algorithms have been developed that, once a quantum computer of sufficient scale is realised, will be able to break classical cryptosystems efficiently (at polynomial-time complexity). A promising method of securing information against this threat at the physical layer has emerged in the form of quantum key distribution (QKD). QKD exploits the fundamental physical properties of light to guarantee information-theoretic security. Research into the application and standardisation of QKD to secure satellite backhaul, however, is still in its infancy. This paper presents a brief overview of the theoretical basis for QKD, whilst also providing a survey of contemporary QKD protocols. It evaluates the ability of these protocols to secure satellite backhaul in the context of a typical satellite-IoT network architecture. Furthermore, it highlights the vulnerabilities, as well as the technical challenges associated with this endeavour. Finally, it proposes directions for future research and development into protocols and standardisation for the satellite-integrated IoT domain. Several challenges must be overcome before QKD can evolve into a global-scale solution for securing satellite-IoT. Secret key generation rate remains very low in practical demonstrations of trusted-relay QKD satellite architectures. Further research is needed to overcome or mitigate the fundamental rate-distance trade-off before satellite QKD can be considered practicable in an IoT application. Alternatives that do not rely on trusted nodes are contingent on nascent technologies such as quantum repeaters and quantum memory. Whilst in theory QKD provides perfect information-theoretic security, it remains vulnerable to attacks that exploit imperfections in real-world equipment. Further effort is needed to develop QKD protocols that can safeguard against the aforementioned challenges.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

Cognitive radio (CR) is considered as a key enabling communication technology that offers efficient wireless connectivity to Internet of Things (IoT) devices. Its integration with the future 5G architecture is expected to advance the IoT paradigm. Security attacks can severely degrade the performance of such CR‐based IoT (CRIoT) networks. The effect of security attacks can be reduced by implementing defensive approaches. However, such solutions come at the expense of degrading spectrum efficiency and consuming more network resources. In this paper, we investigate the spectrum sharing and access problem in a multidevice single‐transceiver CRIoT network with time‐critical applications under jamming attacks. Our main goal is to maximize the number of simultaneously served IoT devices over all available idle channels while ensuring delay requirement, hardware, link quality, security attacks, and spectrum utilization constraints. This problem is formulated as a total unimodular binary linear programming, which is shown to be solvable in polynomial time. Unlike most of previous security‐aware channel assignment solutions that conduct the channel assignment sequentially, our solution simultaneously provides secured distributed channel‐assignment decisions for multiple CRIoT links (batching method). Batching can be realized through an admission control stage for CR IoT devices to announce their control packets. Simulation results reveal that our solution significantly improves network performance compared to previous security‐aware schemes.

The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

The Internet of Things (IoT) now underpins critical systems like healthcare monitors, smart home, smart grids, automotive V2V & V2X networks, and industrial controls but it still relies on classical cryptography, whose security assumptions are vulnerable to quantum computers. A large quantum computer could run Shor’s algorithm to break RSA/ECC and use Grover’s algorithm to accelerate brute-forcing of symmetric keys, undermining the confidentiality and authentication of data in these IoT applications. Researchers have proposed quantum cryptographic solutions. Quantum key distribution (QKD) and quantum random number generators (QRNGs) exploit quantum mechanics to detect eavesdropping and deliver provably secure key material. For example, chip-scale QRNGs can be embedded in IoT sensors to generate true random keys, boosting resistance to brute-force attacks. These quantum techniques provide information-theoretic secrecy, for instance, QKD-generated keys have been used to authenticate smart-grid SCADA. However, most IoT devices are resource-constrained, making direct deployment of conventional QKD hardware challenging. Post-quantum cryptography (PQC) complements quantum methods by providing algorithms such as lattice-based, hash-based that resist quantum attacks on existing hardware. Standardized PQC can be deployed via software updates on legacy IoT nodes, enabling scalable and quantum-safe security within current constraints. Researchers are exploring lightweight PQC for connected cars and e-health sensors. Hybrid approaches combining QKD/QRNG with PQC offer a layered defense. Since QKD protocols are regarded as ‘future-proof’ and PQC standards mature, their combined use will be essential for robust, quantum-safe IoT security. Miniaturizing quantum components such as integrated QKD/QRNG modules and optimizing lightweight PQC schemes for low-power IoT devices are future research directions to secure IoT networks.

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.

Extensive internet of things (IoT) devices being in-fected by malware are an increasingly important viable objective in IoT cyberattacks e.g., botnet, virus, trojan, etc. The botnets got leverage from unsecured IoT devices (e.g., CCTV, Raspberry Pi, Arduino Uno, ESP8266, etc) that operates by using the Internet traffic. In recent year, the high-profile IoT device’s vendor and the researcher from all over universities are exploring the robustness of IoT devices against botnet attacks. This research uses a deep learning approach to prevent botnet attacks on IoT networks. The deep residual one-dimensional CNN (1DCNN) model as the proposed method is used for botnet traffic detection. Two algorithms are provided: data processing for the N-BaIoT dataset and IoT botnet detection training and testing. For data processing, training, and testing, the datasets were evaluated, and the model was optimized with different optimizers. This research used RMS Prop, ADaDelta, AdaGrad, AdaMax, and Adam as optimizers and the CNN was compared with LSTM, CNN with RNN, and Deep residual 1DCNN, respectively. The results showed that Deep Residual 1DCNN with Adam has the highest training accuracy of 88.67%, 88.67% for validation accuracy, and 88.53%for test accuracy.

The cell-free (CF) network architecture has promising applications in Internet of Things (IoT) networks due to its dynamic and adaptive nature in resource allocation. However, due to the openness of wireless channels and the information interaction of sensitive data, the security issues of CF IoT networks have become more severe, especially in the case of powerful opponents. In this article, a high-level secure transmission technique is utilized to protect the privacy of users, which is called covert communication. Specifically, in the CF IoT network conceived, one IoT device secretly transmits sensitive information to an access point (AP) under the cover of a selected jammer to avoid being detected by other curious IoT devices, which are regarded as potential eavesdroppers (Eves) and determined by the central processing unit (CPU) based on greedy degree. Then, we analyze the joint impact of jamming and greedy degree on the detection error probability (DEP) for Eve, and determine Eve’s minimum DEP as well as the corresponding optimal threshold. To enhance energy efficiency while ensuring communication security, three jamming scheduling schemes have been proposed, namely, the jamming scheduling for both the receiver (Bob) and Eve (JSBE), the jamming scheduling for Eve (JSE), and the jamming scheduling for Bob (JSB). Numerical results indicate that greedy degree dominates the average minimum DEP for Eve when IoT devices tend to be lightweight. Furthermore, JSBE scheme demonstrates superiority in terms of covert rate, covert outage probability (COP), and covert energy efficiency (CEE), which provides an effective solution for high-security transmission of energy-constrained nodes in CF IoT networks.

Background. With the proliferation of Internet of Things (IoT) networks in the digital age, the risk of cyberattacks, especially DDoS attacks, is also increasing. IoT devices such as smart refrigerators, thermostats, webcams and other Internet-connected home appliances are being targeted by attackers who can use them as part of a botnet to conduct DDoS attacks. These devices often have inadequate network security and are rarely updated, making them vulnerable. DDoS attacks can result in significant losses such as lost revenue, reputational damage and costs to restore services. So, the vulnerability of IoT networks to DDoS attacks and the need to develop effective protection measures is a pressing issue. Objective. The purpose of the paper is to analyse software that supports DDoS attacks in IoT networks. Provide general recommendations to help improve approaches to defence measures in IoT networks against DDoS-enabled malware. Methods. Five main DDoS attack models are considered: agent-handler model, reflexive model, IRC-based model, web-based model, and P2P-based model. Three most dangerous software capable of DDoS attacks on IoT networks are analyzed: Mirai, XOR.DDoS and Linux.Hydra. Results. There are many models and methods of DDoS attacks on IoT networks. The most dangerous are Mirai, XOR.DDoS and Linux.Hydra. Each of these software has its own specific characteristics and methods of carrying out attacks. The study also showed that there are several effective measures to counter these attacks, including setting strong passwords, regularly updating software, setting up traffic filters and restricting network access. Conclusions. Key aspects of DDoS attacks, their models and process steps are considered. The paper focuses on the three most dangerous software used to conduct such attacks and provides recommendations on how to counteract them.

The ability to deploy such systems throughout a region, or perhaps a nation, will increase as IoT infrastructure develops. Although Internet of Things (IoT) devices are capable of offering intelligent services, the massive amounts of data they gather and analyze pose a severe security threat. Researchers have spent a lot of work building sophisticated NIDS to prevent the exploitation of IoT data in smart applications. However, present methods may be vulnerable to attack, particularly new attacks, due to a lack of sufficient and evenly distributed attack data used to train the detection strategy. This study introduces a original hierarchical adversarial attack (HAA) generation strategy, which in turn targets the convolutional neural network-based intrusion detection in IoT devices with a limited budget, and therefore realizes the level-aware black-box adversarial attack approach. To generate adversarial instances with little disturbance, an intelligent approach is developed that relies on a saliency map methodology. To do this, a shadow CNN framework has been constructed. Using a random walk having restart based hierarchical node selection approach, a subset of the targeted IoT network's nodes with the lowest structural strength are selected. The proposed HAA generation technique is evaluated in comparison to three reference methods using the publicly available data set UNSW-SOSR2019. In a comparison of the two most sophisticated convolutional neural network (CNN) designs, GCN and JK-Net, it was shown that the classification accuracy for NIDS in IoT settings may be reduced by more than 30%.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute‐force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC‐IDS‐2017, and BOT‐IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state‐of‐the‐art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

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