scholarly journals Malicious JavaScript Detection Based on Bidirectional LSTM Model

2020 ◽  
Vol 10 (10) ◽  
pp. 3440
Author(s):  
Xuyan Song ◽  
Chen Chen ◽  
Baojiang Cui ◽  
Junsong Fu
Keyword(s):  
Semantic Information ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Short Term ◽  
Detection Model ◽  
Potential Risks ◽  
Long Short Term Memory ◽  
Almost All ◽  
Cross Site Request Forgery ◽  
Cross Site

JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%.

Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4
Author(s):  
Zadid Khan ◽  
Mashrur Chowdhury ◽  
Mhafuzul Islam ◽  
Chin-Ya Huang ◽  
Mizanur Rahman
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Short Term Memory ◽  
Attack Detection ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Long Short Term Memory ◽  
Vehicle Network

scholarly journals COVID-19 in Iran: Forecasting Pandemic Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rahele Kafieh ◽  
Roya Arian ◽  
Narges Saeedizadeh ◽  
Zahra Amini ◽  
Nasim Dadashi Serej ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Percentage Error ◽  
Mean Square ◽  
Short Term ◽  
Average Percentage ◽  
The Usa ◽  
Long Short Term Memory ◽  
Average Percentage Error ◽  
Almost All

COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R 2 . The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R 2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.

scholarly journals Deep stair walking detection using wearable inertial sensor via long short-term memory network

2020 ◽  
Vol 9 (1) ◽  
pp. 238-246
Author(s):  
Gan Wei Nie ◽  
Nurul Fathiah Ghazali ◽  
Norazman Shahar ◽  
Muhammad Amir As'ari
Keyword(s):  
Short Term Memory ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Window Size ◽  
Sensor Data ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Long Short Term Memory ◽  
Stair Walking

This paper proposes a stair walking detection via Long-short Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.

scholarly journals Network Intrusion Detection Based on an Improved Long-Short-Term Memory Model in Combination with Multiple Spatiotemporal Structures

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaolong Huang
Keyword(s):  
Intrusion Detection ◽  
Short Term Memory ◽  
Network Intrusion Detection ◽  
Short Term ◽  
Detection Model ◽  
Existing Problems ◽  
Network Intrusion ◽  
The Neural Network ◽  
Long Short Term Memory ◽  
Better Than

Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection. The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples. It can not only retain the overall/nonlocal characteristics of the data samples but also extract the most essential deep features of the data samples. Finally, the extracted features are used as input of the LSTM model to realize classification and identification for intrusion samples. Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.

scholarly journals A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans

2020 ◽  
Author(s):  
Nhan T. Nguyen ◽  
Dat Q. Tran ◽  
Nghia T. Nguyen ◽  
Ha Q. Nguyen
Keyword(s):  
Intracranial Hemorrhage ◽  
Short Term Memory ◽  
Ct Scans ◽  
Single Model ◽  
Short Term ◽  
Novel Method ◽  
Long Short Term Memory ◽  
Almost All ◽  
The Relationship

AbstractWe propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture is trained end-to-end with input being an RGB-like image formed by stacking 3 different viewing windows of a single slice. We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset. For the RSNA challenge, our best single model achieves a weighted log loss of 0.0522 on the leaderboard, which is comparable to the top 3% performances, almost all of which make use of ensemble learning. Importantly, our method generalizes very well: the model trained on the RSNA dataset significantly outperforms the 2D model, which does not take into account the relationship between slices, on CQ500. Our codes and models will be made public.

scholarly journals Comparação entre LSTM e CLCNN na detecção de requisições maliciosas em ataques na web

2021 ◽  
Author(s):  
Rafael Bosse Brinhosa ◽  
Marcos A. Michels Schlickmann ◽  
Eduardo da Silva ◽  
Carlos Becker Westphall ◽  
Carla Merkle Westphall
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Cross Site

Com o uso de aplicações web em ambientes dinâmicos de computação em nuvem integrados com dispositivos IoT, os ataques de injeção de SQL e de XSS (Cross-Site Scripting) continuam causando problemas para a segurança. A detecção de requisições maliciosas a nível de aplicação representa um desafio na pesquisa, que está evoluindo usando técnicas de Machine Learning e redes neurais. Este trabalho apresenta a comparação entre duas arquiteturas de aprendizado de máquina usadas para detectar requisições web maliciosas: LSTM (Long Short-Term Memory) e CLCNN (Character-level Convolutional Neural Network). Os resultados demonstram que a CLCNN é a mais eficaz em todas as métricas, com uma acurácia de 98,13%, precisão de 99,84%, taxa de detecção em 95,66% e com um F1-score de 97,70%.

A proposed algorithm based on long short-term memory network and gradient boosting for aeroengine thrust estimation on transition state

2021 ◽  
pp. 095441002199330
Author(s):  
Yong-Ping Zhao ◽  
Yao-Bin Chen ◽  
Zhi-Qiang Li
Keyword(s):  
Steady State ◽  
Transition State ◽  
Short Term Memory ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Direct Thrust Control ◽  
Almost All

Aeroengine thrust estimation is an important problem for direct thrust control since it is unmeasurable. Many methods and algorithms have been proposed to solve this problem. Unfortunately, almost all these methods can only estimate aeroengine thrust when the engine is in steady state. Hence, this study proposes an algorithm based on long short-term memory networks and gradient boosting for aeroengine thrust estimation in transition state. The newly proposed algorithm can estimate thrust of an aeroengine when its working state is changed from one steady state to another. The experimental results demonstrated that the proposed algorithm can be well applied to estimate aeroengine thrust in transition state and the estimated precision can meet the requirements of thrust estimation.

scholarly journals Developing a Long Short-Term Memory-Based Model for Forecasting the Daily Energy Consumption of Heating, Ventilation, and Air Conditioning Systems in Buildings

2021 ◽  
Vol 11 (15) ◽  
pp. 6722
Author(s):  
Luis Mendoza-Pittí ◽  
Huriviades Calderón-Gómez ◽  
José Manuel Gómez-Pulido ◽  
Miguel Vargas-Lombardo ◽  
José Luis Castillo-Sequera ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Short Term ◽  
Term Memory ◽  
International Agencies ◽  
Daily Energy ◽  
Long Short Term Memory ◽  
Air Conditioning Systems

Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.

scholarly journals Attention Neural Network for Biomedical Word Sense Disambiguation

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Chun-Xiang Zhang ◽  
Shu-Yang Pang ◽  
Xue-Yao Gao ◽  
Jia-Qi Lu ◽  
Bo Yu
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Short Term Memory ◽  
Word Sense Disambiguation ◽  
Semantic Category ◽  
Word Sense ◽  
Short Term ◽  
Part Of Speech ◽  
Sense Disambiguation ◽  
Long Short Term Memory

In order to improve the disambiguation accuracy of biomedical words, this paper proposes a disambiguation method based on the attention neural network. The biomedical word is viewed as the center. Morphology, part of speech, and semantic information from 4 adjacent lexical units are extracted as disambiguation features. The attention layer is used to generate a feature matrix. Average asymmetric convolutional neural networks (Av-ACNN) and bidirectional long short-term memory (Bi-LSTM) networks are utilized to extract features. The softmax function is applied to determine the semantic category of the biomedical word. At the same time, CNN, LSTM, and Bi-LSTM are applied to biomedical WSD. MSH corpus is adopted to optimize CNN, LSTM, Bi-LSTM, and the proposed method and testify their disambiguation performance. Experimental results show that the average disambiguation accuracy of the proposed method is improved compared with CNN, LSTM, and Bi-LSTM. The average disambiguation accuracy of the proposed method achieves 91.38%.

Anaphoricity Determination of Anaphora Resolution in Uygur Pronoun Based on CNN-LSTM Model

2017 ◽  
Vol 16 (03) ◽  
pp. 1750016 ◽  
Author(s):  
Tian Shengwei ◽  
Li Dongbai ◽  
Yu Long ◽  
Feng Guanjun ◽  
Zhao Jianguo ◽  
...  
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Short Term Memory ◽  
Feature Representation ◽  
Semantic Feature ◽  
Support Vector ◽  
Anaphora Resolution ◽  
Short Term ◽  
Long Short Term Memory

As a core subtask in anaphora resolution, anaphoricity determination has aroused the interest of researchers. However, in recent work, the influence caused by the deep semantic information and the context of the coreference elements have not been taken into account. In this paper, by combining the semantic feature of Uygur, we established a Convolutional Neural Network & Long Short-Term Memory (CNN_LSTM) model in determining the anaphoricity of Uygur pronoun. Firstly, the deep negative semantic feature representation is extracted via word2vec. Secondly, the shallow explicit feature representation of coreference elements is extracted by our system. Afterwards, two kinds of features are combined to recognize whether coreference element is referential or not. The results showed that the method we used can distinguish coreference element accurately, the ACC[Formula: see text] score is 90.18% and the ACC[Formula: see text] score is 89.93%, which are higher than ANN (Artificial Neural Network) and SVM (Support Vector Machine) respectively.

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