scholarly journals Automatic Email Alert on the Internet of Things-based Smart Motion Detection System

2020 ◽  
Author(s):  
Nur Widiyasono ◽  
Alam Rahmatulloh ◽  
Helmi Firmansah
Keyword(s):  
Internet Of Things ◽  
Motion Detection ◽  
Detection System ◽  
The Internet ◽  
Motion Detection System ◽  
The Internet Of Things

Wristband-Style Pulse and Motion Detection System Based on the Internet of Things

2013 ◽  
Vol 339 ◽  
pp. 59-63
Author(s):  
Chun Yan Hou ◽  
Li Geng Yu ◽  
Kang Ping Yao ◽  
Xue En Li
Keyword(s):  
Internet Of Things ◽  
Motion Detection ◽  
Large Scale ◽  
Detection System ◽  
Dynamic State ◽  
The Internet ◽  
Pulse Detection ◽  
Motion Detection System ◽  
Internet Of Things Technology ◽  
The Internet Of Things

At present, the pulse detection devices and most of the studies are mainly for personal application, there is no such device of pulse detection suitable for the large-scale groups,and the wristband-style pulse detection device is also a very difficult kind of pulse detection devices, the accuracy of them is not high enough in the dynamic state,and the stability is not strong enough. In this paper, we introduce a wristband-style pulse and motion detection system which is fit for large scale group and its stability is strong. We also propose an more effective pulse algorithm in the system,especially for the dynamic state. Also, we employ the Internet of Things technology to realize attendance and positioning management for people. Now the system has been used in some schools and works well.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

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.

scholarly journals Sistem Pendeteksi Ancaman Keamanan Rumah Dengan Menggunakan Telegram Berbasis Internet Of Things

2021 ◽  
Vol 20 (1) ◽  
pp. 127-132
Author(s):  
Fadilah Eka Prasetiyo ◽  
Didik Setiyadi Setiyadi
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Detection System ◽  
Security System ◽  
The Internet ◽  
Threat Detection ◽  
Security Threat ◽  
Home Owner ◽  
Long Time ◽  
The Internet Of Things

The comfort and safety of a house is the dream of any home owner, even a house that has a modern security system will be more in demand than a house with an ordinary security system. By utilizing existing technology, it is possible to create an excellent security system from theft and fire. In order to overcome these problems, a prototype of a security threat detection system was made using telegrams based on the internet of things. This can minimize the inconvenience of home owners when they are not at home in a long time, such as the owner of the house going out of town or abroad. The design of this smart home uses the NodeMCU ESP8266 Wifi Module as a controller, the telegram application as a notification when an unknown person opens a door or window, and when a fire occurs. The sensor used to detect the security of burglars is a Magnetic Door Switch, this sensor is placed on doors and windows. The sensor used to detect fire indications is the Flame Sensor which is placed on the ceiling of the house

scholarly journals PERANCANGAN PROTOTYPE ALAT PENDETEKSI KEBOCORAN GAS LPG BERBASIS ARDUINO UNO R3 DENGAN MODUL SIM800L DAN ESP8266 SEBAGAI MEDIA INFORMASI

2020 ◽  
Vol 4 (2) ◽  
pp. 47-53
Author(s):  
Afdal Eka Kurniawan ◽  
Mayda Waruni Kasrani ◽  
A Asni B
Keyword(s):  
Internet Of Things ◽  
Mobile Phone ◽  
Data Transfer ◽  
Detection System ◽  
The Internet ◽  
Liquefied Petroleum Gas ◽  
Gas Cylinder ◽  
Gas Leak ◽  
Arduino Microcontroller ◽  
The Internet Of Things

Abstract— The LPG (Liquefied Petroleum Gas) gas leak detection and monitoring system is an anticipatory measure for safety from hazards such as gas cylinder explosions. This research aims to build a detection system by integrating MQ-6 sensor, Arduino microcontroller and ESP8266. Data can then be displayed on the Blynk application and notifications will be given via the application and email. The process of sending data using SIM800L module GSM/GPRS is a part that serves to communicate between the main monitors with mobile phone and the module ESP8266E ESP 12E WIFI IOT for data transfer in WIFI network . And this system has fulfilled the rules of the Internet of things, making it easier for the community in its application.

Pengembangan Smart Home Dengan Microcontrollers ESP32 Dan MC-38 Door Magnetic Switch Sensor Berbasis Internet of Things (IoT) Untuk Meningkatkan Deteksi Dini Keamanan Perumahan

2019 ◽  
Vol 3 (3) ◽  
pp. 451-457
Author(s):  
Andi Setiawan ◽  
Ade Irma Purnamasari
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
Early Warning ◽  
Smart Home ◽  
Detection System ◽  
The Internet ◽  
Magnetic Switch ◽  
Web Framework ◽  
Closed Angle ◽  
The Internet Of Things

The objective developed from this research is to utilize Smart Home with an integrated ESP32 microcontroller with a camera and MC-38 door magnetic switch sensor based on the Internet of Things (IoT) as a research base to detect the security of arumsari earth housing in Cirebon District when left by its inhabitants. ESP32 microcontroller which can be programmed via arduino IDE, then functioned to respond to the integrated camera so that it can transmit images when the MC-38 sensor door magnetic switch sensor is active. Technically the combination of the ESP32 microcontroller and MC-38 door magnetic switch sensor, which was developed as a prototype in this study is called the arumsari housing early detection system. The mechanism of the arumsari housing early detection system is when a house door or window is successfully forcibly broken without going through the system mechanism, then automatically an image or can also be developed into a video from a camera mounted on an ESP32 microcontroller will send the image through a web framework or smartphone as a form early warning of security to housing owners. The results obtained from this study are at the angle of normally open MC-38 door magnetic switch sensor of 60 - 1800, will work sending an image signal which means there is an indication of a burglar or unknown person entering the house. Whereas at the normally closed angle MC-38 door magnetic switch sensor is 00-50, it will not work sending an image signal which means the house is safe.

scholarly journals An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1011
Author(s):  
Ahmed Adnan ◽  
Abdullah Muhammed ◽  
Abdul Azim Abd Ghani ◽  
Azizol Abdullah ◽  
Fahrul Hakim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Concept Drift ◽  
The Internet ◽  
Ongoing Research ◽  
Active Research ◽  
The Internet Of Things

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

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