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作者： ahoussou kouassi rodrigue 安徽农业大学

学位级别：硕士

Forecasting stock market prices have always been challenging task for many business analyst and *** fact,stock market price prediction is an interesting area of research for *** successful investment,many investors are interested in knowing about the future situation of the *** prediction systems indirectly help traders by providing supportive information such as the future market *** mining techniques are effective for forecasting future by applying various algorithms to *** researchers have contributed in this area of chaotic forecast in their *** many year different method have been used for resolving many problem in this *** this project,we study the problem of stock market forecasting using Recurrent Neural Network(RNN)with Long Short-Term Memory(LSTM).The purpose of this project is to examine the feasibility and performance of LSTM in stock market *** test the performance of the LSTM with other types of *** Neural Network is trained on the stock quotes using the Back propagation Algorithm which is used to predict share market closing *** our work we use different inputs to build our recurrent neural network to see what the impact on the different predictions *** Accuracy of the performance of the neural network is compared with other accuracy of different *** also prove that some algorithm are good for prediction in a short period but not as a long *** our work of prediction prices,we got the data on the site of *** our work we use three different type of *** different types of technique are:the first technique used to calculate the loss error is named The technique of standard Average and the linear regression news.;the second we use is called the technique of Exponential Moving Average,and finally we use the last technique we use is called the long Short Term memory Network or also called(LSTM).After using these different methods we p

关键词： Long Short-Term Memory(LSTM) root mean square error(RMSE) prediction stock prices

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Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

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Engineering（科研） 2021年第1期13卷 30-44页

作者： Raogo Kabore Adlès kouassi rodrigue N’goran Olivier Asseu Yvon Kermarrec Philippe Lenca Lastic ESATIC Abidjan C&#244 te d’Ivoire Lab-STICC IMT-Atlantique Brest France

Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks.

关键词： ICS SCADA Unsupervised Feature Learning Deep Learning Anomaly Detection

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