Large Number Of Internet Of Things Devices Research Articles

Advancing XSS Detection in IoT over 5G: A Cutting-Edge Artificial Neural Network Approach

The rapid expansion of the Internet of Things (IoT) and the advancement of 5G technology require strong cybersecurity measures within IoT frameworks. Traditional security methods are insufficient due to the wide variety and large number of IoT devices and their limited computational capabilities. With 5G enabling faster data transmission, security risks have increased, making effective protective measures essential. Cross-Site Scripting (XSS) attacks present a significant threat to IoT security. In response, we have developed a new approach using Artificial Neural Networks (ANNs) to identify and prevent XSS breaches in IoT systems over 5G networks. We significantly improved our model’s predictive performance by using filter and wrapper feature selection methods. We validated our approach using two datasets, NF-ToN-IoT-v2 and Edge-IIoTset, ensuring its strength and adaptability across different IoT environments. For the NF-ToN-IoT-v2 dataset with filter feature selection, our Bilayered Neural Network (2 × 10) achieved the highest accuracy of 99.84%. For the Edge-IIoTset dataset with filtered feature selection, the Trilayered Neural Network (3 × 10) achieved the best accuracy of 99.79%. We used ANOVA tests to address the sensitivity of neural network performance to initial conditions, confirming statistically significant improvements in detection accuracy. The ANOVA results validated the enhancements across different feature selection methods, demonstrating the consistency and reliability of our approach. Our method demonstrates outstanding accuracy and robustness, highlighting its potential as a reliable solution for enhancing IoT security in the era of 5G networks.

Investigation on big data evaluation and visualization of internet of things based on edge computing

With the rapid development and popularization of Internet of Things (IoT) technology, a large number of IoT devices have generated massive amounts of data. In order to efficiently process and analyze this data and extract valuable information from it, the analysis and visualization of Big Data (BD) of the IoT based on Edge Computing (EC) has become a hot topic in current research. How to efficiently process and analyze these data has become a current research hot topic. Based on EC technology, this paper used a solution for BD analysis and visualization of the IoT. This paper used Sobel operator and edge extraction algorithm to analyze the BD analysis and visualization research of the IoT based on EC. Firstly, useful information in the image is extracted through the Sobel operator to improve image quality, in order to better understand and utilize image data; Then, edge extraction algorithms are used to quickly and accurately extract the edge information of the image for subsequent data processing and analysis. This scheme used EC nodes to preliminary process and analyze the data, reduced the burden of the central server, and improved the response speed and real-time performance. At the same time, this article also designed a visualization platform to display the analysis results in the form of charts, making it easy for users to intuitively understand the data situation. The experimental results showed that the score of data and information visualization results based on EC was between 89 and 95. The research results showed that the IoT BD analysis and visualization research method based on EC could effectively improve the efficiency and accuracy of IoT data processing and achieve more intuitive data display through experiments. BD analysis and visualization technology can rely on IoT devices to extract useful information and knowledge, and help users better achieve monitoring, control, and decision support for IoT devices.

IoT Botnet Detection Based on Anomalies of Multiscale Time Series Dynamics

In this work, we propose a solution for detecting botnet attacks in the Internet of Things (IoT) by identifying anomalies in the temporal dynamics of their devices. Given their limited computing capabilities, IoT devices are more vulnerable to attacks than conventional computers. In this scenario, botnets have a high degree of severity since they are used to trigger distributed denial-of-service attacks, which are amplified by a large number of IoT devices. Thus, solutions aiming to identify and mitigate the damage caused by botnets in IoT are urgent and essential. We evaluate the number of packets a device transmits, following a multiscale ordinal patterns transformation, and uses Isolation Forest for anomaly detection. By investigating how devices evolve, we can distinguish between normal and anomalous behaviors. We apply the proposed solution to detect two major botnets for IoT: Mirai and Bashlite. We evaluated our model throughout two experimental setups. The first, using a single model for all devices, reaching 99.5% of accuracy and 99.6% of specificity, and the second, by tuning a model per device, reaching 100% of accuracy. These results show that, with the proper transformation, it is possible to use simple methods for detecting anomalies in IoT devices' behaviors.

Federated malware detection based on many‐objective optimization in cross‐architectural IoT

SummaryWith the rising adoption of the Internet of Things (IoT) across a variety of industries, malware is increasingly targeting the large number of IoT devices that lack adequate protection. Malware hunting is challenging in the IoT due to the variety of instruction set architectures of devices, as shown by the differences in the relevant characteristics of malware on different platforms. There are also serious concerns about resource utilization and privacy leaks in the development of conventional detection models. This study suggests a novel federated malware detection framework based on many‐objective optimization (FMDMO) for the IoT to overcome the problems. First, the framework provides a cross‐platform compatible basis with the federated mechanism as the backbone, while avoiding raw data sharing to improve privacy protection. Second, an intelligent optimization‐based client selection method is designed for four objectives: learning performance, architectural selection deviation, time consumption, and training stability, which leads malware detection to retain a high degree of cross‐architectural generalization while enhancing training efficiency. Based on a large IoT malware dataset we constructed, containing 62,515 malware samples across seven typical architectures, the FMDMO is evaluated comprehensively in three scenarios. The experimental results demonstrate the FMDMO substantially enhances the model's cross‐platform detection performance while preserving effective training and flexibility.

A LIGHTWEIGHT ENCRYPTION ALGORITHM TO SECURE IOT DEVICES

Lightweight cryptography is an effective solution for ensuring confidentiality and privacy in resource-constrained environments, particularly in Internet of Things (IoT) systems. These systems consist of a large number of IoT devices, such as sensor nodes and embedded devices that handle sensitive data and are connected via the Internet, making them susceptible to various types of attacks. Considering the constrained processing capability, memory size, and energy of these devices, this article proposes a lightweight block cipher that aims to provide security for IoT devices, with a focus on achieving efficient software implementation, particularly in devices with 32-bit processors. The structural design of the suggested algorithm is based on Type-2 Generalized Feistel Networks (GFN), which are based on four branches of 32-bit and simple key scheduling. To enhance the diffusion and strength of the GFN structure, a partial permutation layer has been added to the cipher. Moreover, to reduce the implementation cost, a simple round function has been designed using low-cost operations like Addition, XOR, and Rotation. Security of the suggested algorithm has been validated through security analysis, and the results show meeting the criteria for avalanche effect with 50.97% Key Sensitivity and 50.58% Plaintext Sensitivity, and the correlation coefficienttest results indicate a weak correlation between the plaintext and ciphertext, this makes it difficult to retrieve the plaintext from the ciphertext. When designing lightweight algorithms, one of the primary objectives is to enhance performance. To evaluate the algorithm's performance, we tested its execution time, code size, memory consumption, and throughput on real IoT devices, such as ESP8266 and ESP32, as an embedded cryptographic module. Based on the experimental results and security analysis, the suggested algorithm showed a good grade of security and higher performance in encryption and decryption operations than other lightweight algorithms, which makes it capable of providing a framework for securing data in the restricted IoT environment.

Toward Secure Data Sharing for the IoT Devices With Limited Resources: A Smart Contract-Based Quality-Driven Incentive Mechanism

With the rapid deployment of Internet of Things (IoT) devices in various industries and fields, the massive amount of data produced by these devices can yield greater value through sharing. A critical challenge in the data sharing process is ensuring that the data is high quality. However, the quality of data provided by a large number of IoT devices is impacted by the variability of factors contributing to the data quality (DQ). Effective and safe sharing of perception data by the limited resources of IoT devices is a problem worth investigating. In this article, we propose a smart contract–based and DQ-driven incentive mechanism. First, a smart contract is proposed to realize security in the data-sharing process, while the proposed DQ evaluation mechanism ensures the quality of the shared data. Second, a two-layer Stackelberg game of Nested Coalitional(TLSNC) scheme is designed to obtain the maximum overall social welfare according to the trust score obtained during DQ evaluation while satisfying the limitation of loose and insufficient computing resources. Moreover, we designed a smart contract for automatic execution of the data-sharing transaction and used a trusted execution environment (TEE) to complete the security calculation of shared data. Finally, the numerical results reveal the effectiveness of the DQ evaluation mechanism and the security of our TEE-based model. Based on the proposed scheme, sustainable incentives for user participation and high-quality data sharing can be achieved. In addition, our system can significantly improve the overall social welfare compared to traditional solutions.

Efficient Lp Distance Computation Using Function-Hiding Inner Product Encryption for Privacy-Preserving Anomaly Detection.

In Internet of Things (IoT) systems in which a large number of IoT devices are connected to each other and to third-party servers, it is crucial to verify whether each device operates appropriately. Although anomaly detection can help with this verification, individual devices cannot afford this process because of resource constraints. Therefore, it is reasonable to outsource anomaly detection to servers; however, sharing device state information with outside servers may raise privacy concerns. In this paper, we propose a method to compute the Lp distance privately for even p>2 using inner product functional encryption and we use this method to compute an advanced metric, namely p-powered error, for anomaly detection in a privacy-preserving manner. We demonstrate implementations on both a desktop computer and Raspberry Pi device to confirm the feasibility of our method. The experimental results demonstrate that the proposed method is sufficiently efficient for use in real-world IoT devices. Finally, we suggest two possible applications of the proposed computation method for Lp distance for privacy-preserving anomaly detection, namely smart building management and remote device diagnosis.

Sustainable Blockchain-Based Digital Twin Management Architecture for IoT Devices

As the number of IoT devices increases, sustainability is becoming a bottleneck of the production process in industrial systems. As a matter of fact, inefficient management and scarce resources significantly impeded the development of sustainability. In recent years, it has been observed that the digital twin technology plays a promising role in facilitating the interaction between the Internet of Things (IoT) assets and digital services. However, high-fidelity models of digital twins raise the requirement of efficient data flows, which is limited by realistic constraints such as data collection strategy and energy supply. We propose a sustainable data collection and management approach to construct digital twins for physical assets. With this approach, data packets are uploaded to the data brokers, namely agents, by a large number of IoT devices. The challenge lies in the balance between enduring data collection and the information loss associated with the stale data. In this article, we aim to optimize the metrics of data fidelity and reveal delay while guaranteeing both sustainable energy and sustainable information. Additionally, a shareable and sustainable blockchain-based digital twin management architecture is proposed, which does not rely on data exchanges with a single centralized server. Our analytical and simulation results demonstrate the applicability of our proposed architecture.

Resource-Efficient Parallelized Random Access for Reliable Connection Establishment in Cellular IoT Networks.

The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT scenario. In IoT scenario, connection establishment is essential and primary for enabling IoT devices to communicate with centralized unit (e.g., base station (BS)). This connection establishment procedure in cellular networks, random access procedure, is generally operated in a contention-based manner. So, it is vulnerable to simultaneous connection requests from multiple IoT devices to the BS, which becomes worse as the contention participants increase. In this article, we newly propose a resource-efficient parallelized random access (RePRA) procedure for resource-efficiently ensuring reliable connection establishment in cellular-based massive IoT networks. Key features of our proposed technique are twofold: (1) Each IoT device simultaneously performs multiple RA procedures in parallel to improve connection establishment success probability, and (2) the BS handles excessive use of radio resources based on newly proposed two types of redundancy elimination mechanisms. Through extensive simulations, we evaluate the performance of our proposed technique in terms of connection establishment success probability and resource efficiency under various combinations of control parameters. Consequently, we verify the feasibility of our proposed technique for reliably and radio-efficiently supporting a large number of IoT devices.

Exploiting Temporal Side Information in Massive IoT Connectivity

This paper considers the joint device activity detection and channel estimation problem in a massive Internet of Things (IoT) connectivity system, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission in each coherence block. In particular, we propose to leverage the temporal correlation in device activity, e.g., a device active in the previous coherence block is more likely to be still active in the current coherence block, to improve the detection and estimation performance. However, it is challenging to utilize this temporal correlation as side information (SI), which relies on the knowledge about the exact statistical relation between the estimated activity pattern for the previous coherence block (which may be imperfect with unknown error) and the true activity pattern in the current coherence block. To tackle this challenge, we establish a novel SI-aided multiple measurement vector approximate message passing (MMV-AMP) framework. Specifically, thanks to the state evolution of the MMV-AMP algorithm, the correlation between the activity pattern estimated by the MMV-AMP algorithm in the previous coherence block and the real activity pattern in the current coherence block is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the denoiser design under the MMV-AMP framework. Specifically, the SI-based soft-thresholding denoiser with binary thresholds and the SI-based minimum mean-squared error (MMSE) denoiser are characterized for the cases without and with the knowledge of the channel distribution, respectively. Numerical results are given to show the significant gain in device activity detection and channel estimation performance brought by our proposed SI-aided MMV-AMP framework.

Blockchain-based Collaborative Caching Mechanism for Information Center IoT

The development of fifth generation mobile communication (5G) technology and Internet of Things (IoT) has enabled more mobile terminals to access the network and generate huge amounts of information content. This will make it difficult for the traditional IP-based host-to-host model to cope with the demand for massive data transmission, making network congestion an increasingly serious problem. To cope with these problems, a new network architecture, the Information-Centric Networking (ICN), a content network with content caching as one of its most core functions, has emerged. In addition, in the era when 5G and future 6G networks gradually realize the interconnection of everything, the Information-Centric Internet of Things (IC-IoT) based on ICN architecture has emerged, and a large number of IoT devices can use ICN nodes as edge devices to realize collaborative caching. The caching capacity of IC-IoT is directly related to the transmission efficiency and capacity of the whole network, and the performance of IC-IoT caching capacity is the top priority of research in this field. To address the above issues, the research in this paper focuses on deploying blockchains in IC-IoT networks and using the consensus mechanism of custom blockchains to motivate ICN nodes and non-ICN nodes in the network to perform caching collaboratively, which in turn improves the caching capacity of the whole network. The main work of this paper has the following points. First, incentivize IC-IoT collaborative caching based on blockchain consensus mechanism: by deploying blockchain in IC-IoT, rewarding nodes that obtain bookkeeping rights to incentivize network-wide collaborative caching, and designing experiments to compare the caching capacity of the network before and after the incentive; second, improve DPoS consensus mechanism to incentivize collaborative caching: Experiments are designed to compare the incentive capacity of PoW consensus mechanism and improved DPoS consensus mechanism for IC-IoT network collaborative caching, and to select the consensus mechanism with better performance; third, the design and implementation of IC-IoT test bed: write ICN program to form ICN network from basic communication to multiple nodes, and then deploying blockchain on the network for subsequent extension studies. This thesis demonstrates the feasibility of using blockchain for IC-IoT network cache collaborative incentive, and proves that the blockchain incentive method in this paper can improve the throughput of IC-IoT network cache by building a test bed.

Forensic challenges regarding the Internet of Things

The Internet of Things brings many benefits to our society, but its advantages come with a price, namely with a huge opportunity for those who want to commit cybercrime. To fight against this kind of criminality, investigators must use appropriate methods. The Forensics of the Internet of Things (IoT) has evolved from “traditional” Digital Forensics, and includes, among other activities, the identification of the devices related to a certain crime, collecting the digital evidence in a form which can be used in front of the judicial bodies, the analysis, and the extraction of all possible information from the collected data, and the preservation of the evidence. The Forensics of IoT must face specific issues, mainly generated by the extraordinary large number of IoT devices and by the great diversity of these devices. An important problem is that data analysis is very difficult to perform, because of different data formats used by the components of IoT. In the forensic investigations related to the IoT, an important concern refers to privacy protection. Overall, IoT Forensics is a challenging field, but we must search for suitable solutions, because ensuring security for IoT is a very important aim.

RPRIA: Reputation and PUF-Based Remote Identity Attestation Protocol for Massive IoT Devices

The smart city is a vision for urban development that combines new-generation information technologies to improve the quality of life. The Internet of Things (IoT) technology is a key technology of smart city, which provides the support for the automatic collection and transmission of massive information in smart city. However, there are some challenges for the IoT devices in a smart city in terms of security and performance, such as various security vulnerabilities and a large amount of computational overhead. With the explosive growth of the number of IoT devices and the data they generate, it is very necessary to ensure that a large number of IoT devices can establish secure and reliable communication with the central server. In this article, we design a massive IoT device remote identity attestation protocol based on the reputation mechanism and physically unclonable function (PUF). Our protocol can efficiently and securely accomplish the mutual authentication and key agreement between massive IoT devices and central server, and between IoT devices. The Burrows–Abadi–Needham (BAN) logic and a formal verification tool called Scyther are employed to prove the security of our protocol. We also evaluate the performance by comparing our protocol with other related protocols in terms of computational overhead, communication overhead, etc. The security and performance results show that our protocol has ideal security and performance.

Digital-Twin-Aided Product Design Framework For IoT Platforms

The increasing number of products is the trend of current industry. However, the product development process is significantly limited by budget and testing risk. Recently, digital twin (DT) has emerged as a promising industrial paradigm that provides an integrated and cohesive view of the product design process. In this article, we propose a product design framework for Internet of Things (IoT) platforms, namely, DT-aided IoT platform design (DTIPD). This framework considers a large number of IoT devices performing different tasks with machine learning (ML) technologies. Each IoT device constructs a particular ML-based model that deals with its task automatically by feeding related data with labels. The challenges of large-scale network management and ground-truth shortage at the initial stage of product iteration are addressed. We propose a two-level hierarchical learning process using the real-time model status stored at DT servers (DTS), aiming to improve product quality while shortening the development lifecycle. The comprehensive experimental results for both the single-DTS and multiple-DTS scenarios demonstrate the applicability of our framework.

Information Harvesting for Far-Field Wireless Power Transfer

In today's smart world, the Internet of Things (IoT) offers a very large scale of technologies and use cases. An IoT system comprises a huge amount of low-power IoT network elements, sensors, and smart devices connected to each other. For these devices, it is important to have a sufficient level of energy resource for communication and to receive software/firmware updates periodically. In this aspect, wireless power transfer is a popular paradigm to tackle energy limitations. Therein, the battery of IoT devices can be remotely replenished by means of radio-frequency-radiated signaling, referred to as far-field wireless power transfer. Furthermore, harvesting energy from ambient communication signals available in the environment introduces simultaneous wireless information and power transfer technology. These systems face the trade-off between information capacity and energy harvesting efficiency. Accordingly, various signaling design frameworks have been proposed to comply with different system preferences between power and information. In this work, aside from existing trends, we propose a novel concept, information harvesting (IH), which introduces a novel protocol that lies in transmitting information on top of the existing wireless power transfer mechanism. Considering the diversity of IoT networks and the availability of wireless power transfer infrastructure, the proposed IH principle may turn out to be a pivotal methodology that combines physical layer security with wireless power transfer, particularly for cases where a large number of IoT devices require software/firmware updates along with periodical battery recharging needs.

Effective load balancing strategy using fuzzy golden eagle optimization in fog computing environment

Fog computing (FC) is becoming popular for connecting real-time Internet of Things (IoT) applications such as driverless cars and health care. Although fog has low latency compared to the cloud, the fog becomes overloaded when serving a large number of IoT devices. FC struggles while distributing the load to the resources that may result in certain fog nodes being overloaded or underloaded. To overcome these issues, an effective load balancing algorithm named Fuzzy Golden Eagle Load Balancing (FGELB) is proposed. The proposed load balancing strategy consists of three stages, namely: assigning priority to the tasks, resource ranking and scheduling, and power management. Initially, the priority is assigned to the incoming task by considering the deadline time, task size, and predefined priority using a fuzzy algorithm. This prioritization of tasks helps to execute the important task without delay. Then the Golden Eagle Optimization algorithm (GEOA) is used to rank and schedule the resources. This ranking and scheduling help the tasks to be executed with suitable resources. Ranked resources execute the tasks in the resource engine, and the power engine manages the power by enabling/disabling the resources based on their necessity. The performance of the approach is tested and it is compared with the results of existing methods in terms of energy consumption, failure rate, computational cost, communication overhead, average turnaround time, and waiting time. The results show that the proposed FGELB approach achieves higher performance than the existing load balancing approaches.

Offline Scaling of IoT Devices in IOTA Blockchain

An increased pattern of hidden Internet of Things (IoT) devices has been observed. Due to the increased number of security attacks, a large number of IoT devices are disappearing from the public internet. Operating blockchain operations in such ad hoc connectivity becomes challenging. However, multiple past studies have pointed towards IOTA Distributed Ledger Technology (DLT) that closely caters to offline blockchain use cases. However, there has been little to no empirical study or introduction to time bounds on transaction confirmation. Therefore, this study explains what provisions the existing IOTA blockchain has to accommodate the increased pattern of hidden IoT devices, and if IOTA is truly sufficient as a solution. In summary, we approach research questions by analyzing the studies that explore the trend of offline IoT devices and evaluating the relevance of offline blockchains, assessing the IOTA specification and codebase around offline transaction-making capabilities and pointing out some bounds that IOTA blockchain nodes must follow towards incoming transactions. Furthermore, we confirm by experimental runs that outside and within the tight time bounds transactions in offline Tangle can become stale and not get confirmed, and the effective time-bound can be even less. Realizing the need for a better offline blockchain scalability solution.

Balanced Computing Offloading for Selfish IoT Devices in Fog Computing

Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. Offloading tasks to the fog that is closer to IoT users for processing has become a means to ensure that tasks are completed quickly. Fog computing cannot only reduce the congestion of the backbone network but also ensure that the task is completed within the specified time. Since fog resources are limited, there will be resource competition among IoT devices. How to quickly and efficiently make an optimal computation offloading decision for individual selfish IoT devices is a fundamental research issue. This article regards the process of multiple IoT devices competing for fog devices as a game and proposes a distributed computation offloading algorithm. The goal is to optimize the balance of computation delay, energy consumption, and cost for fog nodes. The competition between IoT nodes eventually reaches an equilibrium point, that is the Nash equilibrium point. We prove the existence of Nash equilibrium by Weighted Potential Game. In addition, if a large number of IoT devices select the same node for offloading, which will cause the fog node to run out of power and make some networks unable to work normally. Further, causing part of the network to be paralyzed. Therefore, the paper considers the fairness of offloading to extend the network life cycle. A calculation rate adjustment algorithm is designed for the fairness of offloading to ensure that fog nodes do not run out of power and fail. This paper not only fully considers the performance of the IoT device, but also considers the fairness of the fog. Numerous experiments proved the effectiveness of the proposed algorithm.

Joint Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks

Mobile edge computing (MEC) is a promising solution to support emerging delay-sensitive mobile applications, such as self-driving, augment/virtual reality, and various Internet of Things (IoT) applications. By deploying MEC servers at network edge, e.g., close to cellular base stations (BSs), the computational tasks generated by these applications can be offloaded to edge nodes (ENs) and be quickly executed there. At the same time, with the projected large number of IoT devices, the communication and computational resources allocated to each user can be quite limited, making it challenging to provide low-latency MEC services. In this paper, we investigate the problem of task partitioning and user association in an MEC system, aiming to minimize the average latency of all users. We assume that each task can be partitioned into multiple subtasks that can be executed on local devices (e.g., vehicles), MEC servers, and/or cloud servers; each user can be associated with one of the nearby ENs. The subtasks can be independent of or dependent on each other. For each case, we formulate the joint optimization of task partitioning ratios and user association as a mixed integer programming problem. Each problem is solved by decomposing it into two subproblems. The lower-level subproblem is task partitioning under a given user association, which can be solved optimally. The higher-level subproblem is user association, we propose a dual decomposition-based approach and a matching-based approach to derive near-optimal solutions. Simulation results show that compared to benchmark schemes, the proposed schemes reduce the average latency by about 50% and 40% for the cases of independent and dependent subtasks, respectively.

A network-independent tool-based usable authentication system for Internet of Things devices

In this paper, we introduce a hardware-based system with a protocol realization to authenticate Internet of Things (IoT) devices. DigitalSeal is a novel standalone network-independent authentication tool implemented using an Arduino UNO and various components. DigitalSeal ’s I/O elements read a barcode and display a barcode data and its corresponding HMAC, which are used for authentication. DigitalSeal can manage cryptographic keys securely and provide a data integrity in order to defend against Man-in-the-Middle (MitM) and Man-in-the-Browser (MitB) attacks. Moreover, DigitalSeal can be used in various applications, such as an authentication system or protocol, an online/offline transaction, a login session, and an IoT device authentication. Using DigitalSeal, we propose a new protocol for IoT device authentication, providing various security benefits and reducing the burden of key maintenance for a large number of IoT devices. Our authentication protocol realization with DigitalSeal provides a convenient method for securely managing password for multiple IoT devices, prevents unauthorized IoT devices from connecting to the user’s gateway (an IoT home/enterprise network), and secures the communication between the IoT device and the gateway. Our system and associated protocol are both cost-effective and usable. According to our experiments, most users are able to obtain the authentication credential (the HMAC) within 3 seconds with more than 93% accuracy using DigitalSeal.