CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Approach to Simplify the Development of IoT Systems that Interconnect Embedded Devices Using a Single Program

Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized systems.Nowadays,there are several commercial and research platforms available to simplify the creation of such IoT systems.However,developing these systems can often be a tedious task.To address this challenge,a proposed solution involves the implementation of a unified program or script that encompasses the entire system,including IoT devices functionality.This approach is based on an abstraction,integrating the control of the devices in a single program through a programmable object.Subsequently,the proposal processes the unified script to generate the centralized system code and a controller for each device.By adopting this approach,developers will be able to create IoT systems with significantly reduced implementation costs,surpassing current platforms by more than 10%.The results demonstrate that the single program approach can significantly accelerate the development of IoT systems relying on device communication.

An End-to-End Authentication Scheme for Healthcare IoT Systems Using WMSN

The healthcare internet of things(IoT)system has dramatically reshaped this important industry sector.This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers.The goal is the remote monitoring of a patient’s physiological data by physicians.Moreover,this system can reduce the number and expenses of healthcare centers,make up for the shortage of healthcare centers in remote areas,enable consultation with expert physicians around the world,and increase the health awareness of communities.The major challenges that affect the rapid deployment and widespread acceptance of such a system are the weaknesses in the authentication process,which should maintain the privacy of patients,and the integrity of remote medical instructions.Current research results indicate the need of a flexible authentication scheme.This study proposes a scheme with enhanced security for healthcare IoT systems,called an end-to-end authentication scheme for healthcare IoT systems,that is,an E2EA.The proposed scheme supports security services such as a strong and flexible authentication process,simultaneous anonymity of the patient and physician,and perfect forward secrecy services.A security analysis based on formal and informal methods demonstrates that the proposed scheme can resist numerous security-related attacks.A comparison with related authentication schemes shows that the proposed scheme is efficient in terms of communication,computation,and storage,and therefore cannot only offer attractive security services but can reasonably be applied to healthcare IoT systems.

HCloud:A Trusted JointCloud Serverless Platform for IoT Systems with Blockchain

Cloud computing has been exploited in managing large-scale IoT systems.IoT cloud servers usually handle a large number of requests from various IoT devices.Due to the fluctuant and heavy workload,the servers require the cloud to provide high scalability,stable performance,low price and necessary functionalities.However,traditional clouds usually offer computing service with the abstraction of virtual machine(VM),which can hardly meet these requirements.Meanwhile,different cloud vendors provide different performance stabilities and price models,which fluctuate according to the dynamic workload.A single cloud cannot satisfy all the requirements of the IoT scenario well.The JointCloud computing model empowers the cooperation among multiple public clouds.However,it is still difficult to dynamically schedule the workload on different clouds based on the VM abstraction.This paper introduces HCloud,a trusted JointCloud platform for IoT systems using serverless computing model.HCloud allows an IoT server to be implemented with multiple serverless functions and schedules these functions on different clouds based on a schedule policy.The policy is specified by the client and includes the required functionalities,execution resources,latency,price and so on.HCloud collects the status of each cloud and dispatches serverless functions to the most suitable cloud based on the schedule policy.By leveraging the blockchain technology,we further enforce that our system can neither fake the cloud status nor wrongly dispatch the target functions.We have implemented a prototype of HCloud and evaluated it by simulating multiple cloud providers.The evaluation results show that HCloud can greatly improve the performance of serverless workloads with negligible costs.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

Quranic Script Optical Text Recognition Using Deep Learning in IoT Systems

Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things(IoT)systems,much research has been done in using machine learning methods to recognize IoT sensors data.This is particularly the case for optical character recognition of handwritten scripts.Recognizing text in images has several useful applications,including content-based image retrieval,searching and document archiving.The Arabic language is one of the mostly used tongues in the world.However,Arabic text recognition in imagery is still very much in the nascent stage,especially handwritten text.This is mainly due to the language complexities,different writing styles,variations in the shape of characters,diacritics,and connected nature of Arabic text.In this paper,two deep learning models were proposed.The first model was based on a sequence-to-sequence recognition,while the second model was based on a fully convolution network.To measure the performance of these models,a new dataset,called QTID(Quran Text Image Dataset)was devised.This is the first Arabic dataset that includes Arabic diacritics.It consists of 309,720 different 192×64 annotated Arabic word images,which comprise 2,494,428 characters in total taken from the Holy Quran.The annotated images in the dataset were randomly divided into 90%,5%,and 5%sets for training,validation,and testing purposes,respectively.Both models were set up to recognize the Arabic Othmani font in the QTID.Experimental results show that the proposed methods achieve state-of-the-art outcomes.Furthermore,the proposed models surpass expectations in terms of character recognition rate,F1-score,average precision,and recall values.They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.

Chaos-Based Cryptographic Mechanism for Smart Healthcare IoT Systems

Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and more online security and data hijacking attacks,the confidentiality,integrity and availability of data are considered serious issues in HIoT applications.In this regard,lightweight block ciphers(LBCs)are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance,cost,and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones,and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time,the proposed Lightweight Symmetric Block Cipher(LSBC)exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis,while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks,which proves that our LSBC is a good candidate for image security in HIoT.

Real Time Vehicle Status Monitoring under Moving Conditions Using a Low Power IoT System

In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.

Design and Simulation of IoT Systems Using the Cisco Packet Tracer

Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SOx emissions, converter head temperature, NOx emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.

Design of IoT-based Volatile Organic Compounds Monitoring System

Volatile organic compounds(VOC)gas detection devices based on semiconductor sensors have become a common method due to their low cost,simple principle,and small size.However,with the continuous development of materials science,various new materials have been applied in the fabrication of gas sensors,but these new materials have more stringent requirements for operating temperature,which cannot be met by existing sensor modules on the market.Therefore,this paper proposes a temperature-adjustable sensor module and designs an environmental monitoring system based on the STM32F103RET6 microprocessor.This system primarily utilizes multiple semiconductor gas sensors to monitor and record the concentrations of various harmful gases in different environments.It can also monitor real-time temperature,humidity,and latitude and longitude in the current environment,and upload the data to the Internet of Things via 4G communication.This system has the advantages of small size,portability,and low cost.Experimental results show that the sensor module can achieve precise control of operating temperature to a certain extent,with an average temperature error of approximately 3%.The monitoring system demonstrates a certain level of accuracy in detecting target gases and can promptly upload the data to a cloud platform for storage and processing.A comparison with professional testing equipment shows that the sensitivity curves of each sensor exhibit similarity.This study provides engineering and technical references for the application of VOC gas sensors.

IoT安全课程的“实战化”教材建设

针对当前网络空间安全教学内容正向多逆向少、理论多实践少、案例旧复现难的现状,以IoT安全课程教材建设为例,提出采用理论知识加实践指导、传统纸质加视频教程、实践操作加硬件在环的方式打造新型立体化教材,以覆盖IoT安全中的固件安全、硬件安全以及无线电安全等全链条知识,并阐述物联网安全“实战化”教材规划和建设内容。

IoT感知的业务微流程建模

基于物联大数据赋能的业务流程能够更快更准地感知物理世界并及时做出响应的需求突现,提出一种物联网(Internet of Things,IoT)感知的业务微流程建模方法。首先,以单个IoT对象为中心建模,融合MAPE-K(monitor,analysis,plan,execution and knowledge base,MAPE-K)模型思想,将IoT对象实例生命周期的行为状态与微流程实例状态一一映射,实现对单个IoT对象的环形自动监控和调节;其次,基于从IoT传感设备获取的数据,定义基于SASE+语言的业务规则,提取对业务流程有意义的业务事件,避免了无关事件对宏流程的干扰;最后,通过设计一个微流程建模工具原型系统,结合真实案例分析,验证了提出建模方法的有效性,实现了业务流程与IoT实时流式感知数据的结合,并显著减少了宏流程需要处理的业务事件数量。

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.

Smart Shoes Safety System for the Blind People Based on (IoT) Technology

People’s lives have become easier and simpler as technology has proliferated.This is especially true with the Internet of Things(IoT).The biggest problem for blind people is figuring out how to get where they want to go.People with good eyesight need to help these people.Smart shoes are a technique that helps blind people find their way when they walk.So,a special shoe has been made to help blind people walk safely without worrying about running into other people or solid objects.In this research,we are making a new safety system and a smart shoe for blind people.The system is based on Internet of Things(IoT)technology and uses three ultrasonic sensors to allow users to hear and react to barriers.It has ultrasonic sensors and a microprocessor that can tell how far away something is and if there are any obstacles.Water and flame sensors were used,and a sound was used to let the person know if an obstacle was near him.The sensors use Global Positioning System(GPS)technology to detect motion from almost every side to keep an eye on them and ensure they are safe.To test our proposal,we gave a questionnaire to 100 people.The questionnaire has eleven questions,and 99.1%of the people who filled it out said that the product meets their needs.

DL-Powered Anomaly Identification System for Enhanced IoT Data Security

In many commercial and public sectors,the Internet of Things(IoT)is deeply embedded.Cyber security threats aimed at compromising the security,reliability,or accessibility of data are a serious concern for the IoT.Due to the collection of data from several IoT devices,the IoT presents unique challenges for detecting anomalous behavior.It is the responsibility of an Intrusion Detection System(IDS)to ensure the security of a network by reporting any suspicious activity.By identifying failed and successful attacks,IDS provides a more comprehensive security capability.A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.Using deep learning-based anomaly detection,this study proposes an IoT anomaly detection system capable of identifying relevant characteristics in a controlled environment.These factors are used by the classifier to improve its ability to identify fraudulent IoT data.For efficient outlier detection,the author proposed a Convolutional Neural Network(CNN)with Long Short Term Memory(LSTM)based Attention Mechanism(ACNN-LSTM).As part of the ACNN-LSTM model,CNN units are deployed with an attention mechanism to avoid memory loss and gradient dispersion.Using the N-BaIoT and IoT-23 datasets,the model is verified.According to the N-BaIoT dataset,the overall accuracy is 99%,and precision,recall,and F1-score are also 0.99.In addition,the IoT-23 dataset shows a commendable accuracy of 99%.In terms of accuracy and recall,it scored 0.99,while the F1-score was 0.98.The LSTM model with attention achieved an accuracy of 95%,while the CNN model achieved an accuracy of 88%.According to the loss graph,attention-based models had lower loss values,indicating that they were more effective at detecting anomalies.In both the N-BaIoT and IoT-23 datasets,the receiver operating characteristic and area under the curve(ROC-AUC)graphs demonstrated exceptional accuracy of 99%to 100%for the Attention-based CNN and LSTM models.This indicates that these models are capable of making precise predictions.

IoT-Deep Learning Based Activity Recommendation System

The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical settings.The Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health care.Healthcare Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have increased.The existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports.Patients’health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies.This research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it accordingly.The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures.Through a healthcare DSS,this framework is clever enough to detect and analyze a patient’s data.Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care.The data reported in this study demonstrate the notion.This device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.

EScope:Effective Event Validation for IoT Systems Based on State Correlation

Typical Internet of Things(IoT)systems are event-driven platforms,in which smart sensing devices sense or subscribe to events(device state changes),and react according to the preconfigured trigger-action logic,as known as,automation rules.“Events”are essential elements to perform automatic control in an IoT system.However,events are not always trustworthy.Sensing fake event notifications injected by attackers(called event spoofing attack)can trigger sensitive actions through automation rules without involving authorized users.Existing solutions verify events via“event fingerprints”extracted by surrounding sensors.However,if a system has homogeneous sensors that have strong correlations among them,traditional threshold-based methods may cause information redundancy and noise amplification,consequently,decreasing the checking accuracy.Aiming at this,in this paper,we propose“EScope”,an effective event validation approach to check the authenticity of system events based on device state correlation.EScope selects informative and representative sensors using an Neural-Network-based(NN-based)sensor selection component and extracts a verification sensor set for event validation.We evaluate our approach using an existing dataset provided by Peeves.The experiment results demonstrate that EScope achieves an average 67%sensor amount reduction on 22 events compared with the existing work,and increases the event spoofing detection accuracy.

Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

Health monitoring systems are now required,particularly for essential patients,following the COVID-19 pandemic,which was followed by its variants and other epidemics of a similar nature.Effective procedures and strategies are required,though,to react promptly to the enormous volume of real-time data offered by monitoring equipment.Although fog-based designs for IoT health systems typically result in enhanced services,they also give rise to issues that need to be resolved.In this paper,we propose a two-way strategy to reduce network latency and usewhile increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task assignment.For this,a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT healthmonitoring systems is.The algorithm is analyzed for network usage and latency,and the results reveal 20%–25%improvements compared to the existing methods regarding network usage and latency.

IOT Based Smart Parking System Using Ensemble Learning

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver frustration.Existing car parking systems tend to tackle parking issues in a non-digitized manner.These systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary congestion.To address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing availability.User authentication and automated payments are handled using a quick response(QR)code on entry and exit.Some experiments were done on real data collected for six different locations in Cairo via a live popular times library.Several machine learning models were investigated in order to estimate the occu-pancy rate of certain places.Moreover,a clear analysis of the differences in per-formance is illustrated with the final model deployed being XGboost.It has achieved the most efficient results with a R^(2) score of 85.7%.