scholarly journals Malicious Network Behavior Detection Using Fusion of Packet Captures Files and Business Feature Data

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5942
Author(s):  
Mingshu He ◽  
Xiaojuan Wang ◽  
Lei Jin ◽  
Bingying Dai ◽  
Kaiwenlv Kacuila ◽  
...  
Keyword(s):  
Information And Communication Technologies ◽  
Network Traffic ◽  
Feature Fusion ◽  
Knowledge Exchange ◽  
Decision Fusion ◽  
Network Behavior ◽  
Fusion Model ◽  
One Dimensional ◽  
Behavior Detection ◽  
Malicious Behavior

Information and communication technologies have essential impacts on people’s life. The real time convenience of the internet greatly facilitates the information transmission and knowledge exchange of users. However, network intruders utilize some communication holes to complete malicious attacks. Some traditional machine learning (ML) methods based on business features and deep learning (DL) methods extracting features automatically are used to identify these malicious behaviors. However, these approaches tend to use only one type of data source, which can result in the loss of some features that can not be mined in the data. In order to address this problem and to improve the precision of malicious behavior detection, this paper proposed a one-dimensional (1D) convolution-based fusion model of packet capture files and business feature data for malicious network behavior detection. Fusion models improve the malicious behavior detection results compared with single ones in some available network traffic and Internet of things (IOT) datasets. The experiments also indicate that early data fusion, feature fusion and decision fusion are all effective in the model. Moreover, this paper also discusses the adaptability of one-dimensional convolution and two-dimensional (2D) convolution to network traffic data.

Vision-Based Real-time Human Malicious Behavior Detection

2021 ◽  
Author(s):  
Hajra Binte Naeem ◽  
Muhammad Haroon Yousaf ◽  
Farhan Hassan Khan ◽  
Amanullah Yasin
Keyword(s):  
Real Time ◽  
Behavior Detection ◽  
Malicious Behavior

scholarly journals A Commodity Classification Framework Based on Machine Learning for Analysis of Trade Declaration

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 964
Author(s):  
Mingshu He ◽  
Xiaojuan Wang ◽  
Chundong Zou ◽  
Bingying Dai ◽  
Lei Jin
Keyword(s):  
Machine Learning ◽  
Chinese Text ◽  
Decision Fusion ◽  
High Rate ◽  
Text Recognition ◽  
Fusion Model ◽  
Commodity Trade ◽  
Classification Framework ◽  
Text Information ◽  
Application Fields

Text, voice, images and videos can express some intentions and facts in daily life. By understanding these contents, people can identify and analyze some behaviors. This paper focuses on the commodity trade declaration process and identifies the commodity categories based on text information on customs declarations. Although the technology of text recognition is mature in many application fields, there are few studies on the classification and recognition of customs declaration goods. In this paper, we proposed a classification framework based on machine learning (ML) models for commodity trade declaration that reaches a high rate of accuracy. This paper also proposed a symmetrical decision fusion method for this task based on convolutional neural network (CNN) and transformer. The experimental results show that the fusion model can make up for the shortcomings of the two original models and some improvements have been made. In the two datasets used in this paper, the accuracy can reach 88% and 99%, respectively. To promote the development of study of customs declaration business and Chinese text recognition, we also exposed the proprietary datasets used in this study.

A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification

2010 ◽  
Vol 27 (4) ◽  
pp. 293 ◽  
Author(s):  
UttharaGosa Mangai ◽  
Suranjana Samanta ◽  
Sukhendu Das ◽  
PinakiRoy Chowdhury
Keyword(s):  
Pattern Classification ◽  
Feature Fusion ◽  
Decision Fusion

Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Decision Fusion ◽  
Fusion Model ◽  
Vibration Signals ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

Photoplethysmogram-based Cognitive Load Assessment Using Multi-Feature Fusion Model

2019 ◽  
Vol 16 (4) ◽  
pp. 1-17
Author(s):  
Xiao Zhang ◽  
Yongqiang Lyu ◽  
Tong Qu ◽  
Pengfei Qiu ◽  
Xiaomin Luo ◽  
...  
Keyword(s):  
Cognitive Load ◽  
Feature Fusion ◽  
Fusion Model

scholarly journals Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4723
Author(s):  
Patrícia Bota ◽  
Chen Wang ◽  
Ana Fred ◽  
Hugo Silva
Keyword(s):  
Emotion Recognition ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Electrodermal Activity ◽  
Decision Fusion ◽  
Classification Performance ◽  
Physiological Data ◽  
Systematic Analysis ◽  
Public Datasets

Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

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