Security Issues In Internet Of Things Research Articles

DBPBFT: A hierarchical PBFT consensus algorithm with dual blockchain for IoT

The Internet of Things (IoT) is composed of smart devices connected to a network that can send and receive large amounts of data with other devices, generating a lot of data for processing and analysis. Due to the fact that every transaction in blockchain is recorded, placed in a data block, and added to an immutable and secure data chain, blockchain is becoming one of the most promising solutions for enhancing IoT security issues. As more devices become intelligent, the scale of IoT systems, including residential IoT and industrial IoT, is on the rise. Consequently, the issue of resource consumption, stemming from the escalating system communication overhead, is becoming more pronounced. In order to improve the efficiency of the consensus process for residential IoT and reduce the overhead caused by the consensus process, this paper proposes a hierarchical PBFT consensus algorithm With Dual Blockchain for IoT (DBPBFT). Compared to industrial IoT, DBPBFT is more suitable for residential IoT with small scope and clear data classification. DBPBFT separates the responsibilities of dual chains, improving system scalability while also enhancing blockchain security. A chain is divided into several small groups, each responsible for a type of data, reducing system overhead and communication overhead. To avoid unnecessary view-change as much as possible, before consensus begins, each group will select the current view primary node based on reputation values. The simulation results show that the DBPBFT algorithm is superior to traditional algorithms. In terms of reducing communication overhead, compared with EPBFT and DPNPBFT, DBPBFT has increased by 73.8% and 53.1%, respectively. In terms of consensus efficiency, DBPBFT has improved by 34% compared to DPNPBFT.

DBPBFT: A hierarchical PBFT consensus algorithm with dual blockchain for IoT

The Internet of Things (IoT) is composed of smart devices connected to a network that can send and receive large amounts of data with other devices, generating a lot of data for processing and analysis. Due to the fact that every transaction in blockchain is recorded, placed in a data block, and added to an immutable and secure data chain, blockchain is becoming one of the most promising solutions for enhancing IoT security issues. As more devices become intelligent, the scale of IoT systems, including residential IoT and industrial IoT, is on the rise. Consequently, the issue of resource consumption, stemming from the escalating system communication overhead, is becoming more pronounced. In order to improve the efficiency of the consensus process for residential IoT and reduce the overhead caused by the consensus process, this paper proposes a hierarchical PBFT consensus algorithm With Dual Blockchain for IoT (DBPBFT). Compared to industrial IoT, DBPBFT is more suitable for residential IoT with small scope and clear data classification. DBPBFT separates the responsibilities of dual chains, improving system scalability while also enhancing blockchain security. A chain is divided into several small groups, each responsible for a type of data, reducing system overhead and communication overhead. To avoid unnecessary view-change as much as possible, before consensus begins, each group will select the current view primary node based on reputation values. The simulation results show that the DBPBFT algorithm is superior to traditional algorithms. In terms of reducing communication overhead, compared with EPBFT and DPNPBFT, DBPBFT has increased by 73.8% and 53.1%, respectively. In terms of consensus efficiency, DBPBFT has improved by 34% compared to DPNPBFT.

From protocols to countermeasures: A comprehensive survey into IoT safety

The Internet of Things (IoT) has become a field with enormous potential, effect, and development with the emergence of smart cities, smart houses, and intelligent objects. Computer Information System Company (Cisco Inc). Projects that there will be 51 billion devices which are connected by 2021. However, the majority of those IoT gadgets are simple to alter and attack. These IoT devices are generally more susceptible to assaults than other types of endpoints such as desktops, mobile phones, or tablets because of their limitations in terms of storage, computing, and networking capability. The main IoT security concerns are presented and addressed in this study. Along with the methods utilized for connection, interaction, and administration, we examine and classify common security concerns about the IoT layers of architecture. In addition to the most recent assaults, challenges and innovative approaches, we describe the security necessities of the IoT. In addition, we connect and tabulate IoT safety problems with current literature-based remedies. Above all, we explore that blockchain technology, the software that powers Bitcoin, can play a major role in enabling the resolution of numerous IoT security issues. Difficulties along with unresolved research issues related to IoT security are also listed in this study.

Efficient traffic-based IoT device identification using a feature selection approach with Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm

Internet of Things (IoT) refers to the various devices connected to the Internet, enabling them to communicate and transmit data with each other. The rapid development of the IoT also brings security and other problems in cyberspace. In this case, device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. However, device identification has some challenges on IoT traffic datasets, such as large-scale and high-dimensional sparse datasets, features prone to security vulnerabilities, and identification of IP and non-IP devices, which affects the performance of classifiers. Feature selection can be seen as an effective data preprocessing technique in IoT device identification, which may improve the performance of classification and reduce the computational complexity of IoT device identification. In this paper, we propose a traffic-based IoT device identification model using a novel wrapper feature selection approach based on an improved and efficient method, which we call Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm (LS2-BHBA). Specifically, four improved factors are employed in LS2-BHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. In addition, a binary mechanism is implemented to enhance the suitability of the proposed LS2-BHBA for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that LS2-BHBA can reduce the number of features to 10%, and the classification accuracy can reach 98%, which outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.

Blockchain for Enhancing IoT Privacy and Security

The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues

Exploring IoT Vulnerabilities in a Comprehensive Remote Cybersecurity Laboratory.

With the rapid proliferation of Internet of things (IoT) devices across various sectors, ensuring robust cybersecurity practices has become paramount. The complexity and diversity of IoT ecosystems pose unique security challenges that traditional educational approaches often fail to address comprehensively. Current curricula may provide theoretical knowledge but typically lack the practical components necessary for students to engage with real-world cybersecurity scenarios. This gap hinders the development of proficient cybersecurity professionals capable of securing complex IoT infrastructures. To bridge this educational divide, a remote online laboratory was developed, allowing students to gain hands-on experience in identifying and mitigating cybersecurity threats in an IoT context. This virtual environment simulates real IoT ecosystems, enabling students to interact with actual devices and protocols while practicing various security techniques. The laboratory is designed to be accessible, scalable, and versatile, offering a range of modules from basic protocol analysis to advanced threat management. The implementation of this remote laboratory demonstrated significant benefits, equipping students with the necessary skills to confront and resolve IoT security issues effectively. Our results show an improvement in practical cybersecurity abilities among students, highlighting the laboratory's efficacy in enhancing IoT security education.

Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review

Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks. The impacts of these security threats can be diminished by providing protection towards the different IoT security features. Different technological solutions have been presented to cope with the vulnerabilities and providing overall security towards IoT systems operating in numerous environments. In order to attain the full-pledged security of any IoT-driven system the significant contribution presented by artificial neural networks (ANNs) is worthy to be highlighted. Therefore, a systematic approach is presented to unfold the efforts and approaches of ANNs towards the security challenges of IoT. This systematic literature review (SLR) is composed of three (3) research questions (RQs) such that in RQ1, the major focus is to identify security requirements or criteria that defines a full-pledge IoT system. This question also focusses on pinpointing the different types of ANNs approaches that are contributing towards IoT security. In RQ2, we highlighted and discussed the contributions of ANNs approaches for individual security requirement/feature in comprehensive and detailed fashion. In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. In RQ3, different security mechanisms presented by ANNs especially towards intrusion detection system (IDS) in IoT along with their performances are comparatively discussed. In this research, 143 research papers have been used for analysis which are providing security solutions towards IoT security issues. A comprehensive and in-depth analysis of selected studies have been made to understand the current research gaps and future research works in this domain.

SmartDID: A Novel Privacy-Preserving Identity Based on Blockchain for IoT

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.

Dealing With Security Issues In IoT

IoT (Internet of Things) is related to design of a lot of iinter-related “contrived" gadgets having finite capacities with regard to memory and processing power and design is like an Internet. These are frequently battery-driven, raises requirement for adopting environment-friendly techniques. Between the noteworthy ultimatum that establishing interrelated smart objects accompanies are uniformity and coordination. Many smart objects are presumed to develop and addresses of IPv4 are normally utilized. IPv6 is recognized as a possibility for smartobject transmission. Internet of Things (IoT) is a global network of physical and virtual ‘things’ connected to the internet. All objects have unique IDs that are utilized for identification. IoT is the emerging technology which can change the way we communicate with devices. Hereafter almost every electronic device will be a smart device that will be able to compute and communicate with handheld and other infrastructure gadgets. The Internet of Things utilization builds a lot of security problems, which arises from • Smart objects essence: utilization of cryptographic algorithms which are delicate, with regard to requirements of processing and memory. • Quality protocols utilization and the requirement to reduce the data quantity swapped among nodes. An IoT usage which accumulates or handles consumer distinctive data like bank particulars require a foremost rank of security, while a sensor linked to a station area will have a low rank. It need not be costly to carry out fundamental security steps; but it is significant to determine a security strategy and incorporate recuperation measures in the security problems incidents. The main aim of this topic is to describe mechanisms which can help to deal with IoT security issues.

Fuzzy Hashing on Firmwares Images: A Comparative Analysis

With the fast development of the Internet of Things (IoT) technology, there are more attacks against IoT devices, and IoT security issues have attracted considerable attention. Due to the widespread phenomenon that different IoT firmwares reuse the same code, code similarity comparison is an important technique for IoT security analysis. Fuzzy hashing generates fingerprints of files by converting them into intermediate expressions and hashing such expressions, evaluating the fingerprint similarity and thus evaluating the similarity of files that are not identical. In this article, we analyze and compare today’s most widely used fuzzy hashing tools, and classify them in detail. In addition, we also analyze the advantages and disadvantages of different algorithms used by these fuzzy hashing tools. Finally, we select a few of the most convincing fuzzy hashing tools, such as ssdeep and TLSH, for performance comparison by experimental analysis on real firmware datasets.

A Robust and Optional Privacy Data Aggregation Scheme for Fog-Enhanced IoT Network

With the advances in fog computing, various users’ data, collected by smart devices in the Internet of Things (IoT), are published to facilitate services and efficiency. This leads to the users’ worry about security and privacy issues in fog-enhanced IoT. Although privacy protection can ease such worry, users need to pay some extra price, such as more computation costs. Besides, there are some users who want convenience rather than privacy. They prefer to publish their raw data to exchange for better convenience or benefit. In this article, an optional privacy-preserving data aggregation scheme based on BGN homomorphic encryption is proposed, without any trusted third party. In the proposed scheme, each user can choose either no privacy encryption or privacy encryption to upload its own data according to its own privacy sensitivity. With the help of the fog node, the control center can get the aggregated data of all smart devices and the single data of smart devices that choose the no privacy option. In addition, the proposed scheme is analyzed to achieve correctness, privacy preservation, and robustness. Experimental results and comparisons show the proposed scheme is satisfactory in terms of computation cost and communication overhead.

QSec-RPL: Detection of version number attacks in RPL based mobile IoT using Q-Learning

Internet of Things (IoT) has revolutionized the networking by connecting the real world entities to the Internet. IoT connects the communication devices and has an incredible impact on perspective analytics on the massive volume of data produced every day. An attacker may exploit vulnerabilities of IoT entities and compromise users’ security and privacy. Development of solutions to address security and privacy issues of IoT is in premature stage and considered as challenge. This challenge becomes more critical when the devices in the network are resource-constrained in terms of energy, processing and memory. The IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) has emerged in recent years as an adaptation layer to carry IPv6 packets over IEEE 802.15.4. Many IoT applications use Routing Protocol for Low Power and Lossy Networks (RPL) as a network layer protocol developed for routing in 6LoWPAN. Security is challenging in resource constrained environment where encryption may not be a viable solution. Version number attack is one of the most common network layer attacks against RPL based 6LoWPAN. The RPL specification does not address the integrity of the version number and therefore leaves version number mechanism as a weak point in terms of security. This paper investigates the impact of version number attack in RPL networks while considering mobility of the sensor nodes. We propose a solution that utilizes Q-Learning strategy to detect the malicious nodes that are performing version number attack. The proposed approach detects malicious nodes with reasonable accuracy while imposing significantly less overhead on the nodes of low power and lossy networks. There are other approaches too like Message Authentication Codes (MAC) based on symmetric keys but these techniques have memory and communication overhead. So we propose different approach Q-Learning to detect the attacker nodes.

Intrusion Detection for Electric Vehicle Charging Systems (EVCS)

The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet-connected Electric Vehicle Charging Stations (EVCSs) provide a rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem remotely via cyberattacks is rising at the same rate as physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to determine if each traffic stream is legitimate or malicious, that is, binary classification. Furthermore, the IDS can identify the type of malicious traffic, which is called multiclass classification. In this paper, we address IoT security issues in EVCS by using different machine learning techniques and using the native IoT dataset to discover fraudulent traffic in EVCSs, which has not been performed in any previous research. We also compare different machine learning classifier algorithms for detecting Distributed Denial of Service (DDoS) attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the paper. We compare classification algorithms that are placed in line with the traffic and contain DDoS attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the number of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem.

Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness, and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources. Deep learning (DL) algorithms are increasingly integrated into IoT applications to learn and infer patterns and make intelligent decisions. However, current IoT paradigms rely on centralized storage and computing to operate the DL algorithms. This key central component can potentially cause issues in scalability, security threats, and privacy breaches. Federated learning (FL) has emerged as a new paradigm for DL algorithms to preserve data privacy. Although FL helps reduce privacy leakage by avoiding transferring client data, it still has many challenges related to models’ vulnerabilities and attacks. With the emergence of blockchain and smart contracts, the utilization of these technologies has the potential to safeguard FL across IoT ecosystems. This study aims to review blockchain-based FL methods for securing IoT systems holistically. It presents the current state of research in blockchain, how it can be applied to FL approaches, current IoT security issues, and responses to outline the need to use emerging approaches toward the security and privacy of IoT ecosystems. It also focuses on IoT data analytics from a security perspective and the open research questions. It also provides a thorough literature review of blockchain-based FL approaches for IoT applications. Finally, the challenges and risks associated with integrating blockchain and FL in IoT are discussed to be considered in future works.

Secure authentication protocol for IoT applications based on blockchain technology

The lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.

Security Awareness Model for Artificial Intelligence and Internet of Things

With human engagement replacing artificial intelligence in the provision of services, the position of intelligence systems is rapidly changing. The Internet of Things (IOT) plays a prominent role in these variations as it is a cutting-edge and developing technology that connects physical objects to the virtual world, enabling anytime, everywhere connectivity for anything. A technology known as the Internet of Things (IOT) enables the networked connection and data transfer of physical objects, people, gadgets, cars, and other things. IOT eliminates the need for computer-human contact by speaking directly to the user.This encourages increased communication between entities as a result. It can enhance the fundamental services provided in the industries of transportation, banking, healthcare, and education. Recent analyses have shown that the Internet of Things (IOT) and its related components are very susceptible to security vulnerabilities. Using artificial intelligence approaches, IOT security issues can be rectified (AI). AI develops crucial apps that enable data flow in an IoT environment. Intelligent transmission technique.

BHC-IoT: A Survey on Healthcare IoT Security Issues and Blockchain-Based Solution

The Internet of Things (IoT) has become one of the most widely utilized technologies in the recent decade, particularly in healthcare systems. With the usage of wearable and mobile devices, IoT-based healthcare systems have significantly increased the value of the healthcare sector, resulting in the effective use of health data exchange for enhanced, accurate, and quick diagnosis. These devices communicate, process, compute and monitor real-time scenarios via wireless or wired connections. Such devices are heterogeneous, having limited memory and computational power. The adoption of the IoT system in healthcare raises security and privacy concerns. Healthcare records containing sensitive patient data complicate this system since there is a danger of a privacy breach, privacy leakage, tampering, and so on. Blockchain technology has recently emerged as a promising solution to such breaches and problems. In this study, we consider various challenges to healthcare IoT implementation for widespread practical use, considering the security concerning the IoT layered architecture. We have categorized various layered security issues faced by IoT and specific attacks and security issues faced in communication, computation, and authentication IoT-based healthcare systems. Then we demonstrate how blockchain technology can be a key enabler in resolving numerous healthcare IoT security issues. We concluded that blockchain provides a secure and efficient way to resolve healthcare issues.

RVC: A reputation and voting based blockchain consensus mechanism for edge computing-enabled IoT systems

By deploying edge servers around devices, edge computing brings computing resources far away from the cloud center close to the Internet of Things (IoT), which reduces latency and promotes the rapid development of IoT. Since edge servers and devices (hereafter referred to as NODEs) are highly scattered, blockchain is becoming one of the most promising solutions to enhance security issues for IoT. In the blockchain, a consensus mechanism determines how to achieve an agreement among nodes, hence it is an essential element for the operation and efficiency of the blockchain. However, due to the exponentially increasing of nodes in IoT, the consensus efficiency of the traditional consensus mechanism will be greatly reduced, and due to the lack of detection process for malicious nodes, its security will also reduce. To solve the above problems, in this paper, a Reputation and Voting based Consensus mechanism (RVC) is proposed. To reduce the time consumption of the consensus process, RVC adopts a reputation evaluation algorithm without complex hash calculations to select block proposers, which both consider the behaviors in edge computing and blockchain consensus. To prevent malicious nodes from participating in consensus, a filtering algorithm is designed for RVC, which can detect and filter nodes with malicious behaviors. Simulation results show that, RVC outperforms some traditional work. On time consumption, compared with AirBC and hybrid blockchain, RVC improved by 73.6% and 93.7% respectively. And on consensus security, RVC improved by 14% compared with LVBS. When the network scale and the proportion of malicious nodes change exponentially, RVC shows good scalability in terms of time consumption, successful consensus rate and transaction throughput.

Survey of Secure Communications of Internet of Things with Artificial Intelligence

The Internet of Things(IoT) with artificial intelligence aims to build a network that enables smart devices to communicate autonomously and efficiently. It is one of the fastest-growing fields in the information era. It encompasses billions of smart devices worldwide. With the explosive growth of the number of Internet of Things access devices, the types of access network devices are also complex and diverse, especially the heterogeneity of the network is becoming more and more prominent. Faced with such a huge and complex network architecture and communication environment, the existing network security architecture is facing huge challenges. The paper first analyzes the existing three-tier architecture of the IoT and possible security issues, including DDoS Attack, Replay Attack, SQL Injection, Man-in-The-Middle Attacks, and illegal Access. Internet security solutions, including Security Framework, Security Routing Protocol, Authentication Protocol, Data Encryption Transfer & Storage, Key Exchange Protocol and Intrusion Detection, and then explore the possible applications of quantum computing and quantum communication in IoT security, including Quantum Security Hardware Used in IoT, Quantum Key Distribution (QKD) in IoT, Intrusion Detection During QKD with Deep Learning (DL) and Data Encryption Protocol in IoT Based On Quantum Walks (QW), and finally discussed the opportunities and trends of existing security solutions for the IoT Challenges, these challenges and trends can be used as potential research directions for future IoT security.

Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues.

A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals. The healthcare industry has made tremendous strides in efficiency, affordability, and usefulness as a result of new research options and major cost reductions. This includes instructions (AIoT-based) medical advancements can be both beneficial and detrimental. While the IoT concept undoubtedly offers a number of benefits, it also poses fundamental security and privacy concerns regarding medical data. However, resource-constrained AIoT devices are vulnerable to a number of assaults, which can significantly impair their performance. Cryptographic algorithms used in the past are inadequate for safeguarding IoT-enabled networks, presenting substantial security risks. The AIoT is made up of three layers: perception, network, and application, all of which are vulnerable to security threats. These threats can be aggressive or passive in nature, and they can originate both within and outside the network. Numerous IoT security issues, including replay, sniffing, and eavesdropping, have the ability to obstruct network communication. The AIoT-H application is likely to be explored in this research article due to its potential to aid with existing and different technologies, as well as bring useful solutions to healthcare security challenges. Additionally, every day, several potential problems and inconsistencies with the AIoT-H technique have been discovered.