scholarly journals An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Guanyu Lu ◽  
Xiuxia Tian
Keyword(s):  
Intrusion Detection ◽  
Dimensionality Reduction ◽  
Short Term Memory ◽  
Classification Model ◽  
High Dimensional ◽  
Short Term ◽  
Detection Scheme ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Feature Dimensionality Reduction

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.

scholarly journals Chinese Text Classification Model Based on Deep Learning

2018 ◽  
Vol 10 (11) ◽  
pp. 113 ◽  
Author(s):  
Yue Li ◽  
Xutao Wang ◽  
Pengjian Xu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Chinese Text ◽  
Text Classification ◽  
Short Term Memory ◽  
Classification Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

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.

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