Optimum Outlier Detection in Internet of Things Industries Using Autoencoder

2021 ◽  
pp. 77-92
Author(s):  
Arash Hajikarimi ◽  
Mahdi Bahaghighat
Keyword(s):  
Internet Of Things ◽  
Outlier Detection

scholarly journals Outlier detection in indoor localization and Internet of Things (IoT) using machine learning

2020 ◽  
Vol 22 (3) ◽  
pp. 236-243 ◽  
Author(s):  
Mansoor Ahmed Bhatti ◽  
Rabia Riaz ◽  
Sanam Shahla Rizvi ◽  
Sana Shokat ◽  
Farina Riaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Outlier Detection ◽  
Indoor Localization

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.

A survey on outlier detection in Internet of Things big data

2019 ◽  
pp. 225-272
Author(s):  
Abdullah A. Al-khatib ◽  
Mohammed Balfaqih ◽  
Abdelmajid Khelil
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Outlier Detection

scholarly journals Outlier Detection in IoT Using Generative Adversarial Network

2020 ◽  
pp. 306-311
Author(s):  
B. Joyce Beula Rani ◽  
L. Sumathi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Outlier Detection ◽  
Large Scale ◽  
Detection Methods ◽  
Learning Approach ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Detection Approach ◽  
Iot Devices

Usage of IoT products have been rapidly increased in past few years. The large number of insecure Internet of Things (IoT) devices with low computation power makes them an easy and attractive target for attackers seeking to compromise these devices and use them to create large-scale attacks. Detecting those attacks is a time consuming task and it needs to be identified shortly since it keeps on spreading. Various detection methods are used for detecting these attacks but attack mechanism keeps on evolving so a new detection approach must be introduced to detect their presence and thus blocking their spreading. In this paper a deep learning approach called GAN – Generative Adversarial Network can be used to detect this outlier and achieve 85% accuracy.

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