A Comparative Study of the Most Important Methods for Forecasting the ICT Systems Vulnerabilities
暂无分享,去创建一个
[1] Yaman Roumani,et al. Time series modeling of vulnerabilities , 2015, Comput. Secur..
[2] Kayalvizhi Jayavel,et al. Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building , 2021, Future Internet.
[3] Stefan Broda,et al. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) , 2021, Hydrology and Earth System Sciences.
[4] Gerit Wagner,et al. Forecasting IT security vulnerabilities - An empirical analysis , 2020, Comput. Secur..
[5] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[6] Peter R. Winters,et al. Forecasting Sales by Exponentially Weighted Moving Averages , 1960 .
[7] Rupak Majumdar,et al. Software model checking , 2009, CSUR.
[8] Rodney A. Stewart,et al. ANN-based residential water end-use demand forecasting model , 2013, Expert Syst. Appl..
[9] Siddhant Jain,et al. Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility , 2019, CAAI Trans. Intell. Technol..
[10] Tao Chen,et al. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting , 2015, Expert Syst. Appl..
[11] Ioannis E. Livieris,et al. An Advanced CNN-LSTM Model for Cryptocurrency Forecasting , 2021, Electronics.
[12] José Cristóbal Riquelme Santos,et al. An Experimental Review on Deep Learning Architectures for Time Series Forecasting , 2020, Int. J. Neural Syst..
[13] Ilir Gashi,et al. Vulnerability prediction capability: A comparison between vulnerability discovery models and neural network models , 2019, Comput. Secur..
[14] Mark Gillott,et al. We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network , 2021 .
[15] Krithi Ramamritham,et al. A deep learning framework for building energy consumption forecast , 2021 .
[16] W. Budiharto. Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM) , 2021, Journal of Big Data.
[17] Meng Ma,et al. Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction , 2021, IEEE Transactions on Industrial Informatics.
[18] Pei-Chann Chang,et al. A novel model by evolving partially connected neural network for stock price trend forecasting , 2012, Expert Syst. Appl..