scholarly journals Website Fingerprinting Attacks Based on Homology Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Maohua Guo ◽  
Jinlong Fei
Keyword(s):  
Short Term Memory ◽  
Single Agent ◽  
Small Sample ◽  
Classification Model ◽  
Network Systems ◽  
Anonymous Network ◽  
Robust Model ◽  
Long Short Term Memory ◽  
Encrypted Traffic ◽  
Fingerprinting Attack

Website fingerprinting attacks allow attackers to determine the websites that users are linked to, by examining the encrypted traffic between the users and the anonymous network portals. Recent research demonstrated the feasibility of website fingerprinting attacks on Tor anonymous networks with only a few samples. Thus, this paper proposes a novel small-sample website fingerprinting attack method for SSH and Shadowsocks single-agent anonymity network systems, which focuses on analyzing homology relationships between website fingerprinting. Based on the latter, we design a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) attack classification model that achieves 94.8% and 98.1% accuracy in classifying SSH and Shadowsocks anonymous encrypted traffic, respectively, when only 20 samples per site are available. We also highlight that the CNN-BiLSTM model has significantly better migration capabilities than traditional methods, achieving over 90% accuracy when applied on a new set of monitored sites with only five samples per site. Overall, our experiments demonstrate that CNN-BiLSTM is an efficient, flexible, and robust model for website fingerprinting attack classification.

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 Ans-Assist: Robust Human Fall Detection for Unconstraint Smartphone Positions using Modified Long Short-Term Memory Cell

2019 ◽  
Vol 8 (4) ◽  
pp. 5659-5663
Keyword(s):  
Short Term Memory ◽  
Population Distribution ◽  
Elderly Person ◽  
Memory Cell ◽  
Fall Detection ◽  
Automatic Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Robust Model ◽  
Long Short Term Memory

In many aging countries, where the population distribution has shifted to old ages, the need for automatic monitoring devices to help an elderly person when they fall is very crucial. Smartphone is one of the best candidate devices for detecting fall because accelerometer and gyroscope sensors embedded in it respond based on human movements. People usually carry their smartphone in any position and can make fall detection method difficult to detect when fall occurs. This research explored the model for unconstraint human fall detection by using the sensors embedded in smartphone for carried/wearable- sensor-based method. We proposed robust model called Ans-Assist using modified cell of Long Short-Term Memory based model as fall recognition model which can detect human fall from any smartphone position (unconstraint). Some experimental results showed that Ans-Assist achieved 0.95 (± 0.028) average accuracy value using unconstraint smartphone positions. This model can adapt the input from accelerometer and gyroscope sensors which are responsive when human fall.

scholarly journals Deep Nearest Neighbor Website Fingerprinting Attack Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Maohua Guo ◽  
Jinlong Fei ◽  
Yitong Meng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Research Methods ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Concept Drift ◽  
Small Sample ◽  
K Nearest Neighbor ◽  
Encrypted Traffic ◽  
Fingerprinting Attack

By website fingerprinting (WF) technologies, local listeners are enabled to track the specific website visited by users through an investigation of the encrypted traffic between the users and the Tor network entry node. The current triplet fingerprinting (TF) technique proved the possibility of small sample WF attacks. Previous research methods only concentrate on extracting the overall features of website traffic while ignoring the importance of website local fingerprinting characteristics for small sample WF attacks. Thus, in the present paper, a deep nearest neighbor website fingerprinting (DNNF) attack technology is proposed. The deep local fingerprinting features of websites are extracted via the convolutional neural network (CNN), and then the k-nearest neighbor (k-NN) classifier is utilized to classify the prediction. When the website provides only 20 samples, the accuracy can reach 96.2%. We also found that the DNNF method acts well compared to the traditional methods in coping with transfer learning and concept drift problems. In comparison to the TF method, the classification accuracy of the proposed method is improved by 2%–5% and it is only dropped by 3% when classifying the data collected from the same website after two months. These experiments revealed that the DNNF is a more flexible, efficient, and robust website fingerprinting attack technology, and the local fingerprinting features of websites are particularly important for small sample WF attacks.

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 Research on the emotional tendency of web texts based on long short-term memory network

2021 ◽  
Vol 30 (1) ◽  
pp. 988-997
Author(s):  
Xiaojie Li
Keyword(s):  
Public Opinion ◽  
Short Term Memory ◽  
Activation Function ◽  
Classification Model ◽  
Support Vector ◽  
Emotion Classification ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Improved Model

Abstract Through the analysis of emotional tendency in online public opinion, governments and enterprises can stabilize people’s emotion more effectively and maintain social stability. The problem studied in this paper is how to analyze the emotional tendency of online public opinion efficiently, and finally, this paper chooses deep learning algorithm to perform fast analysis of emotional tendency of online public opinion. This paper briefly introduced the structure of the basic model used for emotional tendency analysis of online public opinion and the convolutional neural network (CNN) model used for text emotion classification. Then, the CNN model was improved by long short-term memory (LSTM). A simulation experiment was carried out on MATLAB for the improved text emotion classification model to verify the influence of activation function type on the improved model and the performance difference between the improved model and support vector machine (SVM) and traditional CNN models. The results showed that the improved classification model that adopted the sigmoid activation function had higher accuracy and was less affected by language than the relu and tanh activation functions; the improved classification model had the highest accuracy, recall rate, and F-value in classifying emotional tendency of web texts, followed by the traditional CNN model and the SVM model.

scholarly journals Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition

2021 ◽  
Vol 12 ◽  
Author(s):  
Hua Zhang ◽  
Ruoyun Gou ◽  
Jili Shang ◽  
Fangyao Shen ◽  
Yifan Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Speech Emotion Recognition ◽  
Deep Convolution Neural Network ◽  
Long Short Term Memory ◽  
High Level ◽  
Better Than

Speech emotion recognition (SER) is a difficult and challenging task because of the affective variances between different speakers. The performances of SER are extremely reliant on the extracted features from speech signals. To establish an effective features extracting and classification model is still a challenging task. In this paper, we propose a new method for SER based on Deep Convolution Neural Network (DCNN) and Bidirectional Long Short-Term Memory with Attention (BLSTMwA) model (DCNN-BLSTMwA). We first preprocess the speech samples by data enhancement and datasets balancing. Secondly, we extract three-channel of log Mel-spectrograms (static, delta, and delta-delta) as DCNN input. Then the DCNN model pre-trained on ImageNet dataset is applied to generate the segment-level features. We stack these features of a sentence into utterance-level features. Next, we adopt BLSTM to learn the high-level emotional features for temporal summarization, followed by an attention layer which can focus on emotionally relevant features. Finally, the learned high-level emotional features are fed into the Deep Neural Network (DNN) to predict the final emotion. Experiments on EMO-DB and IEMOCAP database obtain the unweighted average recall (UAR) of 87.86 and 68.50%, respectively, which are better than most popular SER methods and demonstrate the effectiveness of our propose method.

FNDNLSTM

2021 ◽  
pp. 218-232
Author(s):  
Steni Mol T. S. ◽  
P. S. Sreeja
Keyword(s):  
Social Media ◽  
Short Term Memory ◽  
Classification Model ◽  
Fake News ◽  
Term Memory ◽  
The Social ◽  
Social Media Platforms ◽  
Long Short Term Memory ◽  
False News ◽  
Model Technique

In the present scenario, social media platforms have become more accessible sources for news. Social media posts need not always be truthful information. These posts are widely disseminated with little regard for the truth. It is necessary to realize the evolution and origins of false news patterns in order to improve the progression of quality news and combat fake news on social media. This chapter discusses the most frequently used social media (Facebook) and the type of information exchanged to solve this issue. This chapter proposes a novel framework based on the “Fake News Detection Network – Long Short-Term Memory” (FNDN-LSTM) model to discriminate between fake news and real news. The social media news dataset is to be taken and preprocessed using the TF BERT model (technique). The preprocessed data will be passed through a feature selection model, which will select the significant features for classification. The selected features will be passed through the FNDN-LSTM classification model for identifying fake news.

scholarly journals Automatic Subject Classification of Public Messages in E-government Affairs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pei Pan ◽  
Yijin Chen
Keyword(s):  
Short Term Memory ◽  
Automatic Classification ◽  
Attention Mechanism ◽  
Classification Model ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Political Inquiry ◽  
Long Short Term Memory

Abstract Public messages on the Internet political inquiry platform rely on manual classification, which has the problems of heavy workload, low efficiency, and high error rate. A Bi-directional long short-term memory (Bi-LSTM) network model based on attention mechanism was proposed in this paper to realize the automatic classification of public messages. Considering the network political inquiry data set provided by the BdRace platform as samples, the Bi-LSTM algorithm is used to strengthen the correlation between the messages before and after the training process, and the semantic attention to important text features is strengthened in combination with the characteristics of attention mechanism. Feature weights are integrated through the full connection layer to carry out classification calculations. The experimental results show that the F1 value of the message classification model proposed here reaches 0.886 and 0.862, respectively, in the data set of long text and short text. Compared with three algorithms of long short-term memory (LSTM), logistic regression, and naive Bayesian, the Bi-LSTM model can achieve better results in the automatic classification of public message subjects.

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