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Springer eBooks

Quantum Adversarial Artificial Intelligence in Secure Internet of Things Networks

Ilgın Şafak, Tapio Frantti, Fatih Alagöz, Tommi Mikkonen, Majid Haghparast

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-031-99447-0_9

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Physical layer security (PLS) has become the kernel concept for ensuring wireless network security. The main goal of this treatise is to provide a systematic classification of key less schemes. We have classified key less PLS into code, channel adaptation and artificial noise (AN) based approaches and carried out a survey of the diverse schemes employed in literature for key less approach. Further, we have provided the advantages, observations and future directions for each of the techniques. Industry 4.0 revolution with the Internet of Things (IoT) is going to rule the next wireless era with its promising smart home, smart health care, smart retail to name a few. Therefore, we have provided a discussion on the application of key less PLS in IoT. Further, we also discuss about two emerging areas that can be employed for enhancing PLS such as Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI). We have also provided some of the existing security challenges in the IoT application scenario. We have also presented the open research challenges and future directions in the area of key less PLS.
The fast growth of Internet of Things (IoT) networks has heightened the demand for secure, scalable and energy efficient communications methods. Traditional cryptographic techniques are more susceptible to quantum computing attacks and therefore have pushed the necessity for quantum secure methods. This study presents an energy efficient artificial intelligence assisted quantum secure communication framework designed to scale and provide reliable IoT networks. The framework uses Quantum Key Distribution (QKD) combined with deep learning optimization to improve both relationship and network performance. A self-adaptive AI module applies its lower level of computational resources and dynamically changing network conditions in order to optimize key management, routing decisions, and resource allocation, which reduces both energy consumption and communication latency. The proposed system is intended to support the specific types of constraints faced within an IoT environment. The experimental simulation results demonstrate that the framework improves energy efficiency, increases packet delivery ratio, and decreases latency in IVN (Intelligent Vehicular Networks) by comparison with traditional means of secure communication. In addition, by combining AI and quantum communications, we increase our resilience to threats posed by both classical and quantum forms of attack while providing an added layer of protection for transmitting data between devices in future wireless networks. Additionally, this proposed framework will provide a very scalable and effective method for providing IoT solutions within the environment of 5G mobile networks or later generations.
A cluster of interconnected devices able to communicate with each other is denoted as the Internet of Things (IoT). These devices can communicate with each other and exchange data over the Internet without human intervention. A range of daily usable devices, such as wearable devices, smartphones, and home appliances, are programmed in the IoT with sensors and industrial machines in various industries. The proliferation of the IoT is to collect and exchange data for task automation, improving efficiency, and providing new services. Quantum computing incorporates quantum-mechanical phenomena to perform operations on data, allowing for much faster computation than classical computers in certain tasks. This chapter will summarize a concrete idea in the field of artificial intelligence (AI); quantum computing has the qualities of significantly accelerating AI algorithms, particularly in areas such as machine learning, optimization, and simulation. With the integration of the latest techniques, quantum AI will address and solve the complex problems in many applications, such as healthcare, security, and industrial IoT.