LSTM Neural Networks for Detecting Anomalies Caused by Web Application Cyber Attacks

2021 ◽  
Author(s):  
Igor Kotenko ◽  
Oleg Lauta ◽  
Kseniya Kribel ◽  
Igor Saenko
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Web Application ◽  
High Efficiency ◽  
Short Term Memory ◽  
Cyber Attacks ◽  
Long Short Term Memory ◽  
The Many ◽  
Complex Architecture ◽  
Network Anomalies

Detecting anomalies in the traffic of computer networks is an important step in protecting and countering various types of cyber attacks. Among the many methods and approaches for detecting anomalies in network traffic, the most popular are machine learning methods that allow one to achieve high accuracy with minimal errors. One of the ways to improve the efficiency of anomaly detection using machine learning is the use of artificial neural networks of complex architecture, in particular, networks with long short-term memory (LSTM), which have demonstrated high efficiency in many areas. The paper is devoted to the study of the capabilities of LSTM neural networks for detecting network anomalies. It proposes using LSTM neural networks to detect network anomalies caused by cyber attacks to bypass Web Application Firewall vulnerabilities that are very difficult to detect by other means. For this purpose, it is proposed to use LSTM in conjunction with an autoencoder. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirmed the high efficiency of the developed approach. Experiments have shown that the proposed approach allows detecting cyber attacks in real or near real time.

scholarly journals Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 113-121
Author(s):  
Firman Pradana Rachman
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

scholarly journals Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances

2020 ◽  
Vol 12 (11) ◽  
pp. 4471 ◽  
Author(s):  
Jack Ngarambe ◽  
Amina Irakoze ◽  
Geun Young Yun ◽  
Gon Kim
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Indoor Spaces

The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the potential applications of ML algorithms in modeling daylight in indoor spaces and ultimately identify the optimum algorithm. We thus developed and compared the performance of four common ML algorithms: generalized linear models, deep neural networks, random forest, and gradient boosting models in predicting the distribution of indoor daylight illuminances. We found that deep neural networks, which showed a determination of coefficient (R2) of 0.99, outperformed the other algorithms. Additionally, we explored the use of long short-term memory to forecast the distribution of daylight at a particular future time. Our results show that long short-term memory is accurate and reliable (R2 = 0.92). Our findings provide a basis for discussions on ML algorithms’ use in modeling daylight in indoor spaces, which may ultimately result in efficient tools for estimating daylight performance in the primary stages of building design and daylight control schemes for energy efficiency.

scholarly journals Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model

2021 ◽  
Vol 7 ◽  
pp. e645
Author(s):  
Ramish Jamil ◽  
Imran Ashraf ◽  
Furqan Rustam ◽  
Eysha Saad ◽  
Arif Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm. Although various studies have investigated the sarcasm detection on baseline datasets, this work is the first to detect sarcasm from a multi-domain dataset that is constructed by combining Twitter and News Headlines datasets. This study proposes a hybrid approach where the convolutional neural networks (CNN) are used for feature extraction while the long short-term memory (LSTM) is trained and tested on those features. For performance analysis, several machine learning algorithms such as random forest, support vector classifier, extra tree classifier and decision tree are used. The performance of both the proposed model and machine learning algorithms is analyzed using the term frequency-inverse document frequency, bag of words approach, and global vectors for word representations. Experimental results indicate that the proposed model surpasses the performance of the traditional machine learning algorithms with an accuracy of 91.60%. Several state-of-the-art approaches for sarcasm detection are compared with the proposed model and results suggest that the proposed model outperforms these approaches concerning the precision, recall and F1 scores. The proposed model is accurate, robust, and performs sarcasm detection on a multi-domain dataset.

scholarly journals Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies

2021 ◽  
Vol 14 (10) ◽  
pp. 486
Author(s):  
Dante Miller ◽  
Jong-Min Kim
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Machine Learning Methods ◽  
Error Measures ◽  
Long Short Term Memory

In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.

scholarly journals Analysis and Prediction of Unplanned Intensive Care Unit Readmission using Recurrent Neural Networks with Long Short-Term Memory

2018 ◽  
Author(s):  
Yu-Wei Lin ◽  
Yuqian Zhou ◽  
Faraz Faghri ◽  
Michael J. Shaw ◽  
Roy H. Campbell
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Unplanned Readmission ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Machine Learning Models

AbstractBackgroundUnplanned readmission of a hospitalized patient is an extremely undesirable outcome as the patient may have been exposed to additional risks. The rates of unplanned readmission are, therefore, regarded as an important performance indicator for the medical quality of a hospital and healthcare system. Identifying high-risk patients likely to suffer from readmission before release benefits both the patients and the medical providers. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to develop efficient discharge decision-making support system for physicians.Methods and FindingsWe used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate) that are significant in time series with temporal dependencies, which cannot be properly captured by traditional static models, but can be captured by our proposed deep neural network based model. We incorporate multiple types of features including chart events, demographic, and ICD9 embeddings. Our machine learning models identifies ICU readmissions at a higher sensitivity rate (0.742) and an improved Area Under the Curve (0.791) compared with traditional methods. We also illustrate the importance of each portion of the features and different combinations of the models to verify the effectiveness of the proposed model.ConclusionOur manuscript highlights the ability of machine learning models to improve our ICU decision making accuracy, and is a real-world example of precision medicine in hospitals. These data-driven results enable clinicians to make assisted decisions within their patient cohorts. This knowledge could have immediate implications for hospitals by improving the detection of possible readmission. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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