scholarly journals Intelligent Air Pollution Prediction System using Internet of Things (Iot)

2019 ◽  
Vol 9 (1) ◽  
pp. 402-412
Keyword(s):  
Air Quality ◽  
Internet Of Things ◽  
Short Term Memory ◽  
Who Guidelines ◽  
Short Term ◽  
Air Contamination ◽  
Long Short Term Memory ◽  
The Internet Of Things ◽  
Air Pollution Prediction ◽  
New Framework

The internet of Things (IoT) is a path of action interconnected computes multiple procedures, mechanical along with sophisticated machines, things, and individuals to facilitate be certain remarkable identifiers and the ability of trade data over a framework lacking foreseeing human to human and human to machine correspondence, in these paper, an Internet of Things base framework is proposed, in favor of observing natural air contamination and forecast. This framework is able to exist used for observing air contaminations of specific zone and toward Air Quality examination just as gauging the air quality. We Proposed new framework resolve concentrate scheduled the observing of air contaminations, using the blend of IoT with Artificial Intelligence called Artificial Neural Network, and additional explicitly Long Short Term Memory (LSTM). The point in this paper is to discover the best expectation and prediction model for rise or fall of the specific air poisons like O3 , NO2 , SO2 , and CO which are altogether viewed as destructive as indicated by WHO guidelines.

scholarly journals Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Hasan Alkahtani ◽  
Theyazn H. H. Aldhyani
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Short Term ◽  
Security Risks ◽  
Optimal Accuracy ◽  
Long Short Term Memory ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.

scholarly journals Using deep learning to detecting abnormal behavior in internet of things

2022 ◽  
Vol 12 (2) ◽  
pp. 2108
Author(s):  
Mohammed Al-Shabi ◽  
Anmar Abuhamdah
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
High Accuracy ◽  
Abnormal Behavior ◽  
Short Term ◽  
Rapid Pace ◽  
Long Short Term Memory ◽  
The Internet Of Things

<span lang="EN-US">The development of the internet of things (IoT) has increased exponentially, creating a rapid pace of changes and enabling it to become more and more embedded in daily life. This is often achieved through integration: IoT is being integrated into billions of intelligent objects, commonly labeled “things,” from which the service collects various forms of data regarding both these “things” themselves as well as their environment. While IoT and IoT-powered decices can provide invaluable services in various fields, unauthorized access and inadvertent modification are potential issues of tremendous concern. In this paper, we present a process for resolving such IoT issues using adapted long short-term memory (LSTM) recurrent neural networks (RNN). With this method, we utilize specialized deep learning (DL) methods to detect abnormal and/or suspect behavior in IoT systems. LSTM RNNs are adopted in order to construct a high-accuracy model capable of detecting suspicious behavior based on a dataset of IoT sensors readings. The model is evaluated using the Intel Labs dataset as a test domain, performing four different tests, and using three criteria: F1, Accuracy, and time. The results obtained here demonstrate that the LSTM RNN model we create is capable of detecting abnormal behavior in IoT systems with high accuracy.</span>

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110612
Author(s):  
Zhengqiang Ge ◽  
Xinyu Liu ◽  
Qiang Li ◽  
Yu Li ◽  
Dong Guo
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Short Term ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Term Memory ◽  
Protection Mechanism ◽  
Long Short Term Memory

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

scholarly journals Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

2020 ◽  
Vol 12 (6) ◽  
pp. 2570 ◽  
Author(s):  
Thanongsak Xayasouk ◽  
HwaMin Lee ◽  
Giyeol Lee
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Short Term Memory ◽  
Batch Size ◽  
Quality Data ◽  
Short Term ◽  
Optimal Learning ◽  
Term Memory ◽  
Batch Sizes ◽  
Long Short Term Memory

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

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