A NOVEL HYBRID APPROACH FOR NETWORK INTRUSION DETECTION USING EXTREME GRADIENT BOOSTING AND LONG SHORT-TERM MEMORY NETWORKS

2021 ◽  
Vol 8 (4) ◽  
pp. 23
Author(s):  
ERANDIKA RATNAYAKE RASHMI ◽  
USOOF HAKIM ◽  
◽  
Keyword(s):  
Intrusion Detection ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Network Intrusion Detection ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Network Intrusion ◽  
Extreme Gradient Boosting ◽  
Long Short Term Memory

scholarly journals Detection of Cyberattacks Traces in IoT Data

2020 ◽  
Vol 26 (11) ◽  
pp. 1422-1434
Author(s):  
Vibekananda Dutta ◽  
Michał Choraś ◽  
Marek Pawlicki ◽  
Rafał Kozik
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Short Term Memory ◽  
Research Network ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Short Term ◽  
Term Memory ◽  
Network Intrusion ◽  
Long Short Term Memory

Artificial Intelligence plays a significant role in building effective cybersecurity tools. Security has a crucial role in the modern digital world and has become an essential area of research. Network Intrusion Detection Systems (NIDS) are among the first security systems that encounter network attacks and facilitate attack detection to protect a network. Contemporary machine learning approaches, like novel neural network architectures, are succeeding in network intrusion detection. This paper tests modern machine learning approaches on a novel cybersecurity benchmark IoT dataset. Among other algorithms, Deep AutoEncoder (DAE) and modified Long Short Term Memory (mLSTM) are employed to detect network anomalies in the IoT-23 dataset. The DAE is employed for dimensionality reduction and a host of ML methods, including Deep Neural Networks and Long Short-Term Memory to classify the outputs of into normal/malicious. The applied method is validated on the IoT-23 dataset. Furthermore, the results of the analysis in terms of evaluation matrices are discussed.

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 Suspended sediment load prediction using long short-term memory neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nouar AlDahoul ◽  
Yusuf Essam ◽  
Pavitra Kumar ◽  
Ali Najah Ahmed ◽  
Mohsen Sherif ◽  
...  
Keyword(s):  
Neural Network ◽  
Suspended Sediment ◽  
Short Term Memory ◽  
Suspended Sediments ◽  
Gradient Boosting ◽  
Short Term ◽  
Discharge Data ◽  
Term Memory ◽  
Extreme Gradient Boosting ◽  
Long Short Term Memory

AbstractRivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

scholarly journals Application of long short-term memory neural network technique for predicting monthly pan evaporation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mustafa Abed ◽  
Monzur Alam Imteaz ◽  
Ali Najah Ahmed ◽  
Yuk Feng Huang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Short Term Memory ◽  
Maximum Temperature ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Statistical Measures ◽  
Extreme Gradient Boosting ◽  
Long Short Term Memory

AbstractEvaporation is a key element for water resource management, hydrological modelling, and irrigation system designing. Monthly evaporation (Ep) was projected by deploying three machine learning (ML) models included Extreme Gradient Boosting, ElasticNet Linear Regression, and Long Short-Term Memory; and two empirical techniques namely Stephens-Stewart and Thornthwaite. The aim of this study is to develop a reliable generalised model to predict evaporation throughout Malaysia. In this context, monthly meteorological statistics from two weather stations in Malaysia were utilised for training and testing the models on the basis of climatic aspects such as maximum temperature, mean temperature, minimum temperature, wind speed, relative humidity, and solar radiation for the period of 2000–2019. For every approach, multiple models were formulated by utilising various combinations of input parameters and other model factors. The performance of models was assessed by utilising standard statistical measures. The outcomes indicated that the three machine learning models formulated outclassed empirical models and could considerably enhance the precision of monthly Ep estimate even with the same combinations of inputs. In addition, the performance assessment showed that Long Short-Term Memory Neural Network (LSTM) offered the most precise monthly Ep estimations from all the studied models for both stations. The LSTM-10 model performance measures were (R2 = 0.970, MAE = 0.135, MSE = 0.027, RMSE = 0.166, RAE = 0.173, RSE = 0.029) for Alor Setar and (R2 = 0.986, MAE = 0.058, MSE = 0.005, RMSE = 0.074, RAE = 0.120, RSE = 0.013) for Kota Bharu.

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