IoT-CDS:Internet of Things Cyberattack Detecting System Based on Deep Learning Models

The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT environments.To rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT networks.The DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT attacks.The BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal packets.The experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy rates.LSTMs are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack detection.This method,without feature selection,demonstrates advantages in training time and detection accuracy.Consequently,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.

基于NB-IoT的污水管道监测系统设计

针对污水管道监测操作难度大的问题,利用NB-IoT无线通信技术的低功耗、广覆盖和低成本的优势,设计了一种基于NB-IoT的污水管道监测系统,对污水管道内部的甲烷浓度、液位以及温度等数据进行实时采集,并将采集数据通过NB-IOT组网传送至云管理平台,实现数据实时监测。

NB-IoT环境下基于区块链技术的智慧停车诱导系统信任评估研究

为解决智慧停车诱导系统中停车场、车辆、RSU设备之间存在的信息交互不可信及交易不可靠等问题,提出了一种基于区块链技术的信任评估机制。首先,通过对比各种通信方案优缺点,选择窄带物联网作为网络环境,搭建基于区块链技术的智慧停车诱导系统整体框架。其次,基于物联网节点行为考虑选择数据分组转发量、传输数据分组的重复率以及传输数据传输时延作为信任评估指标,引入分段线性函数,建立有等级划分的信任评估模型,结合智能合约形成可靠的交易凭证。最后,对基于区块链技术的停车信息交互过程进行优化,保证了信息交互的安全性。本文为智慧停车诱导系统的信任评估机制研究提供可靠理论基础。

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

The Internet of Things(IoT)access controlmechanism may encounter security issues such as single point of failure and data tampering.To address these issues,a blockchain-based IoT reputation value attribute access control scheme is proposed.Firstly,writing the reputation value as an attribute into the access control policy,and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control;Secondly,storing a large amount of resources fromthe Internet of Things in Inter Planetary File System(IPFS)to improve system throughput;Finally,map resource access operations to qualification tokens to improve the performance of the access control system.Complete simulation experiments based on the Hyperledger Fabric platform.Fromthe simulation experimental results,it can be seen that the access control system can achieve more fine-grained and dynamic access control while maintaining high throughput and low time delay,providing sufficient reliability and security for access control of IoT devices.

Internet of Things (IoT): A Literature Review

One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently.

NB-IoT环境下智慧停车诱导系统停车位选择及诱导方案研究

随着城市化进程的不断加速与机动车数量的持续增长,停车难如今已成为城市可持续健康发展的一个亟待解决的问题。传统停车管理方式效率低下,无法满足现代城市实际停车需求。基于NB-IoT的智慧停车诱导系统通过集成物联网、大数据、云计算和人工智能等现代信息技术,实现了对停车资源的智能化管理和优化配置。本文讨论了NB-IoT技术在智慧停车诱导系统中的应用,并提出一套完整的技术解决方案,并对其进行了相关实证研究和分析。研究结果表明,该系统能够有效提高停车效率,改善用户体验,缓解交通压力,并为未来城市发展与规划提供了价值参考依据。

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

A Few-Shot Learning-Based Automatic Modulation Classification Method for Internet of Things

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

Editorial for Internet of Things (IoT) and Artificial Intelligence (AI) in geotechnical engineering

We are privileged to be invited by the Honorary Editor-in-Chief,Professor Qihu Qian,Editor-in-Chief,Professor Xia-Ting Feng,and the editorial staff of the Journal of Rock Mechanics and Geotechnical Engineering(JRMGE),to serve as Guest Editors for this Special Issue(SI).Over the last decade,the application of the Internet of Things(IoT)and Artificial Intelligence(AI)has increased rapidly to enhance automation in various industries.For efficient construction and maintenance of geotechnical infrastructures(slopes,tunnels,pipelines,and other ground infrastructures),there is a need to access and examine measured data in real-time.Variations in data type due to the usage of unmanned aerial vehicle(UAV)photogrammetric sensors,LiDAR,and fiber optic sensing techniques make data management and analysis more complicated.Advanced artificial intelligence,metaheuristic optimization,and data science can be reliable methods in geotechnical engineering for site investigation,risk assessment,design,construction,and maintenance at a higher level.

Enabling Industrial Internet of Things(IIoT) towards an emerging smart energy system

The increasing penetration of renewable energy on the transmission and distribution power network is driving the adoption of two-way power flow control, data and communications needed to meet the dependency of balancing generation and load. Thus, creating an environment where power and information flow seamlessly in real time to enable reliable and economically viable energy delivery, the advent of Internet of Energy(IoE) as well as the rising of Internet of Things(IoT) based smart systems.The evolution of IT to Io T has shown that an information network can be connected in an autonomous way via routers from operating system(OS) based computers and devices to build a highly intelligent eco-system. Conceptually, we are applying the same methodology to the Io E concept so that Energy Operating System(EOS) based assets and devices can be developed into a distributed energy network via energy gateway and self-organized into a smart energy eco-system.This paper introduces a laboratory based IIo T driven software and controls platform developed on the NICE Nano-grid as part of a NICE smart system Initiative for Shenhua group. The goal of this effort is to develop an open architecture based Industrial Smart Energy Consortium(ISEC) to attract industrial partners, academic universities, module supplies, equipment vendors and related stakeholder to explore and contribute into a test-bed centric open laboratory template and platform for next generation energy-oriented smart industry applications.In the meanwhile, ISEC will play an important role to drive interoperability standards for the mining industry so that the era of un-manned underground mining operation can become the reality as well as increasing safety regulation enforcement.

Securing the Internet of Health Things with Certificateless Anonymous Authentication Scheme

Internet of Health Things(IoHT)is a subset of Internet of Things(IoT)technology that includes interconnected medical devices and sensors used in medical and healthcare information systems.However,IoHT is susceptible to cybersecurity threats due to its reliance on low-power biomedical devices and the use of open wireless channels for communication.In this article,we intend to address this shortcoming,and as a result,we propose a new scheme called,the certificateless anonymous authentication(CAA)scheme.The proposed scheme is based on hyperelliptic curve cryptography(HECC),an enhanced variant of elliptic curve cryptography(ECC)that employs a smaller key size of 80 bits as compared to 160 bits.The proposed scheme is secure against various attacks in both formal and informal security analyses.The formal study makes use of the Real-or-Random(ROR)model.A thorough comparative study of the proposed scheme is conducted for the security and efficiency of the proposed scheme with the relevant existing schemes.The results demonstrate that the proposed scheme not only ensures high security for health-related data but also increases efficiency.The proposed scheme’s computation cost is 2.88 ms,and the communication cost is 1440 bits,which shows its better efficiency compared to its counterpart schemes.

Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.

A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verify their performance,particularly in terms of various evaluation metrics,i.e.,processing,communication,and storage overheads.These results have verified the superiority of the proposed scheme against existing state-of-the-art approaches,preferably in terms of communication,storage,and processing costs.

Internet of Things Authentication Protocols: Comparative Study

Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still thebiggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services providedby an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures,data, and devices. Authentication, as the first line of defense against security threats, becomes the priority ofeveryone. It can either grant or deny users access to resources according to their legitimacy. As a result, studyingand researching authentication issues within IoT is extremely important. As a result, studying and researchingauthentication issues within IoT is extremely important. This article presents a comparative study of recent researchin IoT security;it provides an analysis of recent authentication protocols from2019 to 2023 that cover several areaswithin IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security researchsummary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. Itillustrates that the resistance of protocols against attacks, and their computational and communication cost arelinked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemesand provides some future research directions.

PARE:Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

Convergence Platform of Cloud Computing and Internet of Things (IoT) for Smart Healthcare Application

Internet of Things (IoT) is a widely distributed network which requires small amount of power supply having limited storage and processing capacity. On the other hand, Cloud computing has virtually unlimited storage and processing capabilities and is a much more mature technology. Therefore, combination of Cloud computing and IoT can provide the best performance for users. Cloud computing nowadays provides lifesaving healthcare application by collecting data from bedside devices, viewing patient information and diagnose in real time. There may some concerns about security and other issues of the patient’s data but utilization of IoT and Cloud technologies in healthcare industry would open a new era in the field of healthcare. To ensure basic healthcare needs of the people in the rural areas, we have proposed Cloud-IoT based smart healthcare system. In this system various types of sensors (Temperature, Heart bit, ECG, etc.) are equipped in the patient side to sense the patient’s physiological data. For securing data RSA based authentication algorithm and mitigation of several security threats have been used. The sensed data will process and store in the Cloud server. Stored data can be used by the authorized and/or concerned medical practitioner upon approved by the user for patient caring.

Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.