scholarly journals Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 3187-3196 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Interval Estimation ◽  
Short Term ◽  
Displacement Prediction ◽  
Term Memory ◽  
Time Series Decomposition ◽  
Landslide Displacement Prediction ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Forecasting number of vulnerabilities using long short-term neural memory network

2021 ◽  
Vol 11 (5) ◽  
pp. 4381
Author(s):  
Mohammad Shamsul Hoque ◽  
Norziana Jamil ◽  
Nowshad Amin ◽  
Azril Azam Abdul Rahim ◽  
Razali B. Jidin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Data Points ◽  
Memory Network ◽  
Past Data ◽  
Long Short Term Memory

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.

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