short term
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2022 ◽  
Vol 314 ◽  
pp. 108779
Author(s):  
Hidemitsu Sakai ◽  
Weiguo Cheng ◽  
Charles P. Chen ◽  
Toshihiro Hasegawa

2022 ◽  
Vol 327 ◽  
pp. 107825
Author(s):  
Cristina Lazcano ◽  
Noelymar Gonzalez-Maldonado ◽  
Erika H. Yao ◽  
Connie T.F. Wong ◽  
Jenna J. Merrilees ◽  
...  

2022 ◽  
Vol 198 ◽  
pp. 104702
Author(s):  
Manuel Esteban Lucas-Borja ◽  
Pedro Antonio Plaza-Àlvarez ◽  
S.M. Mijan Uddin ◽  
Misagh Parhizkar ◽  
Demetrio Antonio Zema

Author(s):  
Mohammed Al-Shabi ◽  
Anmar Abuhamdah

<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>


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