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 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 Convolutional Neural Networks with LSTM for Intrusion Detection

10.29007/j35r ◽  
2020 ◽  
Author(s):  
Mostofa Ahsan ◽  
Kendall Nygard
Keyword(s):  
Intrusion Detection ◽  
Hybrid Algorithm ◽  
Short Term Memory ◽  
High Accuracy ◽  
Short Term ◽  
Network Infrastructure ◽  
Network Intrusion ◽  
Novel Approach ◽  
Network Intrusions ◽  
Long Short Term Memory

A variety of attacks are regularly attempted at network infrastructure. With the increasing development of artificial intelligence algorithms, it has become effective to prevent network intrusion for more than two decades. Deep learning methods can achieve high accuracy with a low false alarm rate to detect network intrusions. A novel approach using a hybrid algorithm of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is introduced in this paper to provide improved intrusion detection. This bidirectional algorithm showed the highest known accuracy of 99.70% on a standard dataset known as NSL KDD. The performance of this algorithm is measured using precision, false positive, F1 score, and recall which found promising for deployment on live network infrastructure.

scholarly journals Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Jin
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Short Term Memory ◽  
Wireless Sensor ◽  
Feature Subset ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Network Intrusion

As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation-based intrusion detection model for long- and short-term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long- and short-term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning-based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two-way long- and short-term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long- and short-term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.

scholarly journals HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL

SINERGI ◽  
2020 ◽  
Vol 24 (3) ◽  
pp. 189
Author(s):  
Andi Apriliyanto ◽  
Retno Kusumaningrum
Keyword(s):  
Short Term Memory ◽  
Model Performance ◽  
Average Score ◽  
Negative Effects ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
The Neural Network ◽  
Data Word ◽  
Long Short Term Memory

Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807.  These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate.

scholarly journals Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 411
Author(s):  
Yunkai Zhang ◽  
Yinghong Tian ◽  
Pingyi Wu ◽  
Dongfan Chen
Keyword(s):  
Autism Spectrum Disorder ◽  
Action Recognition ◽  
Short Term Memory ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Short Term ◽  
Term Memory ◽  
Clinical Observations ◽  
The Neural Network ◽  
Long Short Term Memory

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

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