Enhanced Non-parametric Sequence-based Learning Algorithm for Outlier Detection in the Internet of Things

2021 ◽  
Author(s):  
Abel Efetobor Edje ◽  
Shaffie Muhammad Abd Latiff ◽  
Howe Weng Chan
Keyword(s):  
Internet Of Things ◽  
Outlier Detection ◽  
Learning Algorithm ◽  
The Internet ◽  
The Internet Of Things ◽  
Non Parametric

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Outlier Detection Approaches Based on Machine Learning in the Internet-of-Things

2020 ◽  
Vol 27 (3) ◽  
pp. 53-59
Author(s):  
Jinfang Jiang ◽  
Guangjie Han ◽  
Li liu ◽  
Lei Shu ◽  
Mohsen Guizani
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Outlier Detection ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 511 ◽  
Author(s):  
Anuroop Gaddam ◽  
Tim Wilkin ◽  
Maia Angelova ◽  
Jyotheesh Gaddam
Keyword(s):  
Internet Of Things ◽  
Outlier Detection ◽  
Physical World ◽  
Wireless Sensor ◽  
Smart Sensors ◽  
The Internet ◽  
Sensor Faults ◽  
Detection Model ◽  
Near Future ◽  
The Internet Of Things

The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.

Analysis of risk factors in financial supply chain based on machine learning and IoT technology

2020 ◽  
pp. 1-11
Author(s):  
Sun Hongjin
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Supply Chain ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
The Internet ◽  
Supply Chain Risk ◽  
Financing Model ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The financial supply chain is affected by many factors, so an artificial intelligence model is needed to identify supply chain risk factors. This article combines the actual situation of the financial supply chain, improves the traditional machine learning algorithm, and takes the actual company as an example to build a corresponding risk factor recognition model. From the perspective of optimizing the supply chain financial model, this paper combines the functions of the Internet of Things technology and the characteristics of the supply chain financial inventory pledge financing model to design a new type of inventory pledge financing model. The new model makes up for the defects of the original model through the functions of intelligent identification, visual tracking and cloud computing big data processing of the Internet of Things technology. In addition, this study verifies the performance of the system, uses a large amount of data in Internet finance as an object, and obtains the corresponding results through mathematical statistical analysis. The research results show that the model proposed in this paper has a certain effect on the identification and analysis of financial supply chain risk factors.

scholarly journals Deep Learning Model for Air Quality Prediction Based on Big Data

2021 ◽  
pp. 170-175
Author(s):  
P. Parkavi ◽  
S. Rathi
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Air Quality ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Pollution Monitoring ◽  
The Internet ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
The Internet Of Things

Air pollution and its harm to human health has become a serious problem in many cities around the world. In recent years, research interests in measuring and predicting the quality of air around people has spiked. Since the Internet of things has been widely used in different domains to improve the quality for people by connecting multiple sensors. In this work an IOT based air pollution monitoring with prediction system is proposed. The internet of Things is a action interrelated computing devices that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. The deep learning algorithm approach is to evaluate the accuracy for the prediction of air pollution. The main objective of the project is used to predict the air Quality. The large dataset works with LSTM for better air quality prediction. The prediction accuracy of air quality with LSTM, the evaluation indicator Root means square error is chosen to measure performance.

RFID Protocol Design, Optimization, and Security for the Internet of Things

2017 ◽  
Author(s):  
Alex X. Liu ◽  
Muhammad Shahzad ◽  
Xiulong Liu ◽  
Keqiu Li
Keyword(s):  
Internet Of Things ◽  
Design Optimization ◽  
Protocol Design ◽  
The Internet ◽  
The Internet Of Things ◽  
Rfid Protocol

Multimedia system and database simulation based on internet of things and cloud service platform

2020 ◽  
pp. 1-12
Author(s):  
Zhang Caiqian ◽  
Zhang Xincheng
Keyword(s):  
Internet Of Things ◽  
Cloud Service ◽  
Multimedia System ◽  
The Internet ◽  
Multimedia Database ◽  
Cloud Platform ◽  
Service Platform ◽  
Device Management ◽  
Cloud Service Platform ◽  
The Internet Of Things

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.

Review of the Current State and Future Development in Standardising Artificial Lighting

2019 ◽  
pp. 4-44 ◽  
Author(s):  
Peter Thorns
Keyword(s):  
Internet Of Things ◽  
Future Development ◽  
Best Practice ◽  
The Internet ◽  
Artificial Lighting ◽  
Current State ◽  
Internal Structures ◽  
European Regulations ◽  
The Internet Of Things

This paper discusses the organisations involved in the development of application standards, European regulations and best practice guides, their scope of work and internal structures. It considers their respective visions for the requirements for future standardisation work and considers in more detail those areas where these overlap, namely human centric or integrative lighting, connectivity and the Internet of Things, inclusivity and sustainability.

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