Encrypted Cyberattack Detection System over Encrypted IoT Traffic Based onStatistical Intelligence

In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lackof performance of each component, such as computing resources like CPUs and batteries, to encrypt and decryptdata. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomicalfinancial and human casualties. For this reason, the application of encrypted communication to IoT has beenrequired, and the application of encrypted communication to IoT has become possible due to improvements inthe computing performance of IoT devices and the development of lightweight cryptography. The applicationof encrypted communication in IoT has made it possible to use encrypted communication channels to launchcyberattacks. The approach of extracting evidence of an attack based on the primary information of a networkpacket is no longer valid because critical information, such as the payload in a network packet, is encrypted byencrypted communication. For this reason, technology that can detect cyberattacks over encrypted network trafficoccurring in IoT environments is required. Therefore, this research proposes an encrypted cyberattack detectionsystem for the IoT (ECDS-IoT) that derives valid features for cyberattack detection from the cryptographic networktraffic generated in the IoT environment and performs cyberattack detection based on the derived features. ECDSIoT identifies identifiable information from encrypted traffic collected in IoT environments and extracts statisticsbased features through statistical analysis of identifiable information. ECDS-IoT understands information aboutnormal data by learning only statistical features extracted from normal data. ECDS-IoT detects cyberattacks basedonly on the normal data information it has trained. To evaluate the cyberattack detection performance of theproposed ECDS-IoT in this research, ECDS-IoT used CICIoT2023, a dataset containing encrypted traffic generatedby normal and seven categories of cyberattacks in the IoT environment and experimented with cyberattackdetection on encrypted traffic using Autoencoder, RNN, GRU, LSTM, BiLSTM, and AE-LSTM algorithms. Asa result of evaluating the performance of cyberattack detection for encrypted traffic, ECDS-IoT achieved highperformance such as accuracy 0.99739, precision 0.99154, recall 1.0, F1 score 0.99575, and ROC_AUC 0.99822when using the AE-LSTM algorithm. As shown by the cyberattack detection results of ECDS-IoT, it is possibleto detect most cyberattacks through encrypted traffic. By applying ECDS-IoT to IoT, it can effectively detectcyberattacks concealed in encrypted traffic, promoting the efficient operation of IoT and preventing financial andhuman damage caused by cyberattacks.

基于NB-IoT技术的天然气管道泄漏监测系统

为了对天然气管道运行情况进行实时监测,在发生泄漏时能够及时做出反应,设计了基于NBIoT技术的天然气管道泄漏监测系统。系统利用压力变送器、DHT11温湿度传感器和GPS/北斗定位系统分别对管道的内部压力、设备周围的温湿度和经纬度位置进行采集,将采集的数据通过STM32控制NBIoT物联网模块上传至OneNET物联网云平台,最终通过云平台对天然气管道进行实时监测。实验表明:该系统能够对天然气管道的数据进行实时上传,能够通过云平台进行天然气管道状态的实时监测。

Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption

Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges.Concurrently,the Internet of Things(IoT)has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services,ultimately enhancing human lives.This paper presents a new approach utilizing lightweight blockchain technology,effectively reducing the computational burden typically associated with conventional blockchain systems.By integrating this lightweight blockchain with IoT systems,substantial reductions in implementation time and computational complexity can be achieved.Moreover,the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm,renowned for its homomorphic characteristics,to reinforce the privacy and security of IoT-generated data.The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platformfor storing and analyzing sensitive data of the supply chain data.This platformfacilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy.The results validate the robust security of the proposed system,comparable to standard blockchain implementations,leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.

面向NVM的IoT时序数据多态协作压缩策略

压缩策略是影响IoT时序数据存储系统性能的重要因素,而现有压缩策略缺乏针对NVM与IoT时序数据特性的优化机制。因此,提出了面向NVM的IoT时序数据多态协作压缩策略。首先,给出了IoT时序数据的组织结构。然后,针对IoT时序数据在一段时间内较稳定以及在用户态与内核态读写NVM适合的粒度差异较大的情况,设计了分层压缩策略。在用户态接收数据时,采用轻量级的数据压缩算法减少需存储的数据量,也减小了对IoT时序数据的存储效率的影响;针对IoT系统以查询和分析异常时序数据为主的特性,设计了深度压缩算法,在内核态对历史IoT时序数据进行深度压缩。其次,针对深度压缩历史IoT时序数据与存储新接收的IoT时序数据之间对NVM带宽的竞争,提出了写带宽保证的动态调整算法。最后,构建了面向NVM的IoT时序数据多态协作压缩策略原型PCCTSMS,并使用YCSB-TS工具进行测试与分析。实验结果表明,与InfluxDB、OpenTSDB、KairosDB和TVStore相比,PCCTSMS最高能提升161.3%的写吞吐率以及减少14.6%的存储空间。

基于串级模糊PID控制的NB-IoT智能保温箱

针对农产品冷链物流中存在的运力不足和无法混装小批量冷链农产品等问题,提出一种基于NB-IoT技术的智能保温箱解决方案。该方案基于半导体制冷片、PT100温度传感器和NB-IoT技术构建了系统的硬件部分,实现了远程温控和冷链物流追溯,同时利用串级模糊PID算法实现了全程精确控温。此外,基于Simulink搭建了智能保温箱温度控制系统模型,通过仿真对比验证了智能保温箱的温度控制效果。结果显示,与PID算法和模糊PID算法相比,保温箱应用的串级模糊PID算法具有更快的调节速度和更小的超调量,并在受到干扰时表现出更好的稳定性,为农产品冷链物流领域提供了高效可靠的温度控制方案。

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。

元宇宙IoT场景化阅读行为研究

文章基于元宇宙IoT场景化阅读行为具有的互动感知、体验沉浸和生态社群特征,构建元宇宙IoT场景化阅读行为理论模型并对其进行检验,研究元宇宙IoT场景化阅读行为形成机理,分析其与普通网络数字阅读行为的区别,进而提出元宇宙IoT场景化阅读行为促进策略,即以空间有用感知为中心建立用户感知到认知的意愿引导策略,以互动体验沉浸为核心构建信息偶遇的互动触发策略,基于社群生态环境设计社区阅读领袖培养策略。

基于NB-IoT的智慧健康监测系统设计

人口老龄化导致慢性非传染性的患病率会越来越高,智慧健康养老成为国家应对老龄化的战略目标。针对我国目前的人口结构现状,提出一种基于窄带物联网技术(NB-IoT)的智慧健康监测系统。该系统结合NB-IoT技术、传感器技和软件技术,系统主要包括可穿戴数据采集终端、云服务平台、远程监测平台等3部分。数据采集终端以STM32作为主控制器,实现对用户生理信息的采集,通过NB-IoT网络传输到云平台上,通过远程监测平台实现健康的远程监测,对健康问题进行预警。实验结果表明:系统可以实时采集老年人的生理信息(血压、血氧、心率)、运动轨迹以及定位信息,对老年人进行实时全方位健康监控,提前预防并降低慢性疾病发病率、减小死亡率、延长寿命。

基于NB-IoT的地下管廊环境监测系统设计

城市地下管廊内布设了大量的管线,如燃气管道、网络通讯线路、电力线路等,由于地下环境复杂多变,存在着气体泄漏、爆炸、火灾等安全风险。针对这些问题,提出一种基于窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的地下管廊环境监测系统。该系统采用先进的传感器技术、NB-IoT技术、软件技术,系统主要分为数据采集模块、物联网云平台、远程监测系统三部分。数据采集模块以STM32作为主控单元连接各个传感器,采集温度、湿度、水位、可燃气体等数据,经过处理后利用NB-IoT网络上传到物联网云平台,远程监测系统调用物联网云平台的数据接口进行远程显示与预警。实验结果表明,系统在降低系统总体功耗的同时,能够实时、稳定地进行地下管廊环境监测,提前预防可能存在的风险。

IOT2000在高职院校现代电气控制实训中的应用

基于物联网应用的智能网关能够兼容自动控制系统中的不同通信协议及数据源,高效进行数据采集并实现通信互连的特点,研究它在高职自动化专业教育实践中的应用具有非常重要的指导意义。以亚龙YL158GA1为基础构建基于西门子IOT2000的数据采集与传输系统,开展物联网智能网关在高职院校技能训练中的应用研究,并对IOT2000的控制功能进行了探索。最终对IOT2000的优势与不足进行了总结,并结合目前国内高校“双创”教育对IOT2000的应用前景进行了分析。

A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.

基于NB-IoT技术的环境监测系统优化设计

为进一步提升工业生产过程的安全系数,提出一种基于窄带物联网NB-IoT的环境监测系统。其中,以NB-IoT技术作为系统的主要通信,以传感器为主要的环境数据采集工具,以改进的BP神经网络作为预测方法进行环境风险预测。实验结果表明,与传统的BP神经网络相比,经过粒子群算法PSO优化的BP神经网络具有更高的预测精度,且稳定性较好,将其应用于环境风险的预测时误差始终保持在1%的误差范围内。设计的基于NB-IoT的环境监测系统能够进行准确的数据采集和风险预测,能够进一步保障生产安全,可行性较高。

基于NB-IoT的无线抄表系统设计与实现

为了便于管理人员及时准确地采集到用户电能消耗数据,提高工作效率,基于嵌入式与NB-IoT物联网技术,设计出一种无线抄表系统,能够实时采集和监测用户用电情况。该系统以低功耗的STM32系列微控制器作为主控芯片,利用RS485接口的Modbus-RTU通信协议采集电能信息;运用RFID无线射频识别技术进行Mifare卡识别,读取用户信息。基于BC35-G通信模块结合UART串口及AT指令,建立起主控芯片与华为云平台的联系,利用4G网络传输的MQTT通信协议将采集到的数据上传至华为云平台,提高了数据传输的实时性和可靠性,为实现及时准确地采集用户电能消耗数据,提供了解决方案。

面向物联网的NB-IoT信号优化方法研究

随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖不均等挑战。这些挑战不仅影响用户体验,还限制了物联网应用的进一步发展。因此,研究面向物联网的NB-IoT信号优化方法具有重要意义。文章深入研究面向物联网的NB-IoT信号优化方法,提出多种有效的优化策略和技术手段。

NB-IoT和eMTC远程无线物联网通信开发应用

探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。详细介绍基于NB-IoT的智慧水表系统和基于eMTC的车辆跟踪系统的设计与实现,展示这些系统在提高城市管理效率、物流监控等方面的积极作用。针对网络覆盖与信号质量、数据安全与隐私保护、功耗与续航等关键技术挑战,提出相应的解决方案。最后总结NB-IoT和eMTC的广阔应用前景和市场潜力,并对未来技术发展和应用趋势进行展望。

基于NB-IoT的智慧路灯控制器设计

随着智慧城市建设步伐的加快,各大城市必然要对城市路灯照明系统进行节能化和智能化升级。文章针对传统路灯能耗大、人力维护成本高、智能化程度低等问题,设计一种基于NB-IoT的智慧路灯控制器。单灯控制器以微控制器STM32为控制核心,通过NB-IoT通信模组实现路灯与控制中心的组网,从而实现路灯的远程开关及调光控制、路灯数据采集、故障定位等功能。该路灯控制器以实际LED路灯进行测试验证,基于NB-IoT路灯控制器能够满足路灯照明系统的节能化和智能化以及集中管理的需求。

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。

基于NB-IoT的应用层数据安全传输机制

NB-IoT(Narrow Band Internet of Things)是基于蜂窝窄带物联网的一种新兴技术,是物联网的一个重要分支。随着NB-IoT终端设备的规模不断增大,物联网安全面临数据泄露、中间人攻击等安全威胁。本论文针对NB-IoT技术的数据安全传输研究,从物联网终端安全和应用服务安全两方面进行分析,结合密码技术给出了NB-IoT系统安全模型,提出了基于物联网应用层数据信源加密传输机制,给出了轻量级身份认证协议和数据加密传输协议,论证了该方案的安全性,通过实验验证了所提方案的可行性和适用性。

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.