Unnamed Ariel Vehicle-assisted-Internet of Things (UAV-assisted IoT) applications have emerged as a powerful integrated technology, showcasing remarkable results in many domains with numerous advantages. However, this technology encounters several challenges, and security is one of them. Given that, authentication and verification of legitimate devices with data privacy pose key concerns in a wireless communication environment. Even though, the literature highlights the growing security threats of this technology, where attackers can easily compromise the existing authentication and data preservation schemes. Therefore, it is crucial for all involved stakeholders to address these concerns using Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) algorithms, as they offer cost-effective solutions. While some algorithms have been used in the literature to accurately and effectively predict, detect, and prevent vulnerabilities in this technology, they may not adequately handle modern or advanced security threats. Therefore, in this article, we provide a comprehensive survey of the existing literature from 2014 to 2022, specifically focusing on AI, ML, DL, and RL-enabled prototypes. Our goal is to highlight the contributions and limitations of the considered articles. Based on our observations, we will emphasize on the open challenges to set the stage for future research to enhance the security of this emerging technology. Moreover, to bridge this gap of all aspects of this technology, we will discuss layer-wise security threats and countermeasure schemes following the TCP/IP stack. Finally, we will compare our work with existing review articles to demonstrate its novelty, uniqueness, and potential usefulness for the people working in this field.

Unmanned aerial vehicles (UAVs) as an intermediary can offer an efficient and useful communication paradigm for different Internet of Things (IoT) applications. Following the operational capabilities of IoTs, this emerging technology could be extremely helpful in the area, where human access is not possible. Because IoT devices are employed in an infrastructure-less environment, where they communicate with each other via the wireless medium to share accumulated data in network topological order. However, the unstructured deployment with wireless and dynamic communication make them disclosed to various security threats, which need to be addressed for their efficient results. Therefore, the primary objective of this work is to present a comprehensive survey of the theoretical literature associated with security concerns of this emerging technology from 2015-to-2022. To follow up this, we have overviewed different security threats of UAV-aided IoT applications followed by their countermeasures techniques to identify the current challenges and requirements of this emerging technology paradigm that must be addressed by researchers, enterprise market, and industry stakeholders. In light of underscored constrains, we have highlighted the open security challenges that could be assumed a move forward step toward setting the future research insights. By doing this, we set a preface for the answer to a question, why this paper is needed in the presence of published review articles. For novelty and uniqueness, we have performed a comparative analysis section-wise with rival papers to demonstrate that how this paper is different from them.

The complexity of next-generation industrial cyber-physical systems (NG-ICPSs) is increasing due to the integration of machine-embedded sensors, cyber-infrastructure, and physical processes, which calls for the new intelligent operation mechanisms to achieve system-level objectives. Although NG-ICPS has proliferated in many applications, such as advanced manufacturing, intelligent transportation, smart homes, etc., and achieved remarkable results. But these applications are susceptible to many problems and new security threats are some of them that goes beyond the scope of traditional communication and network security, due to the tight integration of cybers and physical systems. For redressal of this, several traditional authentication and data privacy schemes have been used in the recent past, but somehow, they did not satisfy the need for this emerging technology, due to their complex verification and validation processes. Recently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) enabled authentication and data preservation techniques had shown remarkable results to address the security problems of this technology at the system/client side and server side cost-effectively. Given that, in this article, we present a comprehensive survey of the current literature on NC-ICPS technology security threats and their countermeasures, with a focus on AI, ML, and DL-enabled techniques. We evaluate these techniques by identifying their advantages and disadvantages compared to traditional authentication and data preservation methods. In addition, we discussed the review articles published on this topic to acknowledge their contributions and limitations, because most of them cover a specific part of security concerns of this technology, and unable to present the true picture of all problems under one shallow. Building on this, we addressed the gaps in the literature by highlighting the open security challenges of NG-ICPS technology and suggesting potential future research directions, considering the capabilities of AI, ML, and DL-enabled algorithms. Finally, we compared this article sectionwise with rival review articles to claim its novelty followed by the question of reviewers, editors, students, and readers why this article is needed in the presence of these articles and what are its distinctive factor that makes this article different from them.

This work proposes a scheme that combines the advantages of a quantum secret sharing procedure and quantum dialogue. The proposed scheme enables the participants to simultaneously make mutual identity authentications, in a simulated scenario where the boss, Alice, shares a secret with her two agents Bob and Charlie. The secret is protected by checking photons to keep untrustworthy agents and outer attacks from getting useful information. Before the two agents cooperate to recover Alice’s secret, they must authenticate their identity using parts of a pre-shared key. In addition, the whole pre-shared key is reused as part of recovering the secret data to avoid any leaks of information. In comparison with previous schemes, the proposed method can efficiently detect eavesdropping and it is free from information leaks. Furthermore, the proposed scheme proved to be secure against man-in-the-middle attacks, impersonation attacks, entangled-and-measure attacks, participant attacks, modification attacks and Trojan-horse attacks.

We propose a feasible and efficient dynamic multiparty quantum private comparison protocol that is fully secure against participant attacks. In the proposed scheme, two almost-dishonest third parties generate two random keys and send them to all participants. Every participant independently encrypts their private information with the encryption keys and sends it to the third parties. The third parties can analyze the equality of all or some participants’ secrets without gaining access to the secret information. New participants can dynamically join the protocol without the need for any additional conditions in the protocol. We provide detailed correctness and security analysis of the proposed protocol. Our security analysis of the proposed protocol against both inside and outside attacks proves that attackers cannot extract any secret information.