Detection of malware on the internet of things and its applications depends on long short-term memory network

2021 ◽  
Author(s):  
K. Priyadarsini ◽  
Nilamadhab Mishra ◽  
M. Prasad ◽  
Varun Gupta ◽  
Syed Khasim
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
The Internet ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
The Internet Of Things

scholarly journals Long Short Term Memory Network-based Interference Recognition for Industrial Internet of Things

2020 ◽  
pp. 876-885
Author(s):  
Natthanan Promsuk ◽  
◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Internet Of Things ◽  
Short Term Memory ◽  
Minimum Mean Square Error ◽  
Human Beings ◽  
Short Term ◽  
Term Memory ◽  
Industrial Iot ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Iot Devices

Nowadays, the rapid growth of wireless Internet of things (IoT) devices is one of the significant factors leading smart systems in various sectors, such as healthcare, education, and agriculture. This is, of course, not limited to the industrial sector, where the IoT concept is applied for real time monitoring and control of devices instead of human beings. Co-channel interferences occurs when two or more devices are using the same channel. It causes unnecessary contention as the devices will be forced to defer transmissions until the medium is clear causing a loss of throughput. Adjacent channel interference is even more serious and occurs when the devices are on overlapping channels causing corrupted data, which makes indispensable retransmissions. The more devices are added to an environment, the higher the likelihood of interference problem is. Due to a huge number of IoT devices, the interference issue becomes very serious. In this paper, a long short-term memory network-based interference recognition (LSTM-IR) is proposed. This method is integrated into the industrial IoT (IIoT) network in factory environments to mitigate the effect of interferences. The comparative results are done among three interference suppression techniques (IST) including the traditional minimum mean square error (MMSE) approach, the multi-layer perceptron (MLP), and the proposed LSTM-IR. Since the type of transmitting and receiving data is usually a sequencing data type. Therefore, the proposed method with the input data from a fast Fourier transform (FFT) algorithm provides better performances because it is based on an LSTM which is suitable for the sequences of data. The number of the devices in the factory is obviously the key factor because the smaller number of active devices causes less interferences.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

2021 ◽  
Vol 60 ◽  
pp. 608-619
Author(s):  
Xianli Liu ◽  
Shaoyang Liu ◽  
Xuebing Li ◽  
Bowen Zhang ◽  
Caixu Yue ◽  
...  
Keyword(s):  
Tool Wear ◽  
Short Term Memory ◽  
Tool Wear Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Wear Monitoring ◽  
Memory Network ◽  
Long Short Term Memory
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