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An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

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Computers, Materials & Continua 2020年第10期65卷 355-367页

作者： Ye Wang Bixin Liu Hongjia Wu Shan Zhao Zhiping Cai Donghui Li cheang chak fong College of Computer National University of Defense TechnologyChangsha410073China Academy of Military Science Beijing100091China College of Information and Intelligence Hunan Agricultural UniversityChangsha410128China Faculty of Information Technology Macao University of Science and Technology999078Macao

With the continuous development of e-commerce,consumers show increasing interest in posting comments on consumption experience and quality of ***,people make purchasing decisions relying on other comments much more than ever *** the reliability of commodity comments has a significant impact on ensuring consumers’equity and building a fair ***,some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their *** improper ways of self-profiting have severely ruined the entire online shopping *** to detect and prevent these deceptive comments effectively,we construct a model of Multi-Filters Convolutional Neural Network(MFCNN)for opinion spam *** is designed with a fixed-length sequence input and an improved activation function to avoid the gradient vanishing problem in spam opinion ***,convolution filters with different widths are used in MFCNN to represent the sentences and *** experimental results show that MFCNN outperforms current state-of-the-art methods on standard spam detection benchmarks.

关键词： Opinion spam detection deceptive reviews deep learning convolutional neural network activation function

Implementation of a Remote Control System for Smart Home

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信息通信技术 2007年第1期1卷 44-49页

作者： YUAN Xiao-Chen cheang chak-fong LI Jian-Qing Faculty of Information and Technology Macao University of Science and Technology Macao SAR China

Advances in mobile communication bring great effects on people’s life styles. This paper describes the implementation of a remote control scheme, Remote Control System via Bluetooth and SMS (Short Message Service), which controls smart home in short distance and long distance, respectively. Although SMS is widely used for a variety of applications, it is not suitable for the purpose of remote control because it suffers from transmission delay, loss and lacks of confidentiality. This paper proposes a SMS-based protocol, which is designed with the mechanisms of reliable transmission and information encryption, thus it is capable of the implementation of secure and reliable control.

关键词：存储管理遥控装置维护技术霍姆休姆

无线多跳网络中具中继负载平衡预估的路由算法

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小型微型计算机系统 2014年第4期35卷 689-693页

作者：郑泽峰潘艺萍吴建平清华大学计算机科学与技术系北京100084 澳门科技大学资讯科技学院

无线多跳网络中通信节点利用相邻的中继节点转发数据,当节点因移动而需切换中继时,决定中继选择的路由协议对通信性能起关键作用.本文在具有基础设施的混合式无线多跳网络中,提出一种以OLSR路由算法为基础来构建最小生成树中继拓扑的MST... 详细信息

无线多跳网络中通信节点利用相邻的中继节点转发数据,当节点因移动而需切换中继时,决定中继选择的路由协议对通信性能起关键作用.本文在具有基础设施的混合式无线多跳网络中,提出一种以OLSR路由算法为基础来构建最小生成树中继拓扑的MST-OLSR路由算法,通过引入对中继负载预估的机制,减少各节点通信中在上行链路所需的中继跳数的同时,平衡了中继间的流量负载.仿真结果表明,与传统的OLSR相比,MST-OLSR可以有效提高移动节点中继切换的时延及丢包的通信性能.

关键词：无线多跳网络路由算法中继切换负载平衡 OLSR

Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory

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Computers, Materials & Continua 2018年第11期57卷 297-306页

作者： chak fong cheang Yiqin Wang Zhiping Cai Gen Xu Faculty of Information Technology Macao University of Science and TechnologyMacao College of Computer National University of Defense TechnologyChangsha410073China

Cloud computing provides easy and on-demand access to computing resources in a configurable *** flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines(VMs),instead of being restricted on a single physical *** more and more network services are deployed on the cloud,the detection of the intrusion likes Distributed Denialof-Service(DDoS)attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud *** this paper,we propose a cloud-based intrusion detection system(IDS)which inspects the features of data flow between neighboring VMs,analyzes the probability of being attacked on each pair of VMs and then regards it as independent evidence using Dempster-Shafer theory,and eventually combines the evidence among all pairs of VMs using the method of evidence *** the traditional IDS that focus on analyzing the entire network service externally,our proposed algorithm makes full use of the internal interactions between VMs,and the experiment proved that it can provide more accurate results than the traditional algorithm.

关键词： Intrusion detection cloud computing Dempster-Shafer theory evidence fusion

Analyzing the Structure and Connectivity ofContinent-Level Internet Topology

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Computers, Materials & Continua 2019年第6期59卷 955-964页

作者： Xionglve Li Junhua Xi Zhiping Cai Tao Yang chak fong cheang National University of Defense Technology College of ComputerChangsha410073China Macao University of Science and Technology 999078Macao

The research and analysis of Internet topology is hot in the field of networkmeasurement, which have important applications in network security, traffic schedulingand many other fields. Most of the existing works are focused on the AS-level and routerleveltopology, but few works are about the IP-level topology. In fact, obtaining thetopology of each continent and knowing how the topologies of the continents areconnected to each other can help us understanding the Internet around the world morethoroughly. In this paper, we obtained data sets from RIPE, constructed and analyzednetwork topologies of all the continents. By analyzing the topological connectionsbetween continents, we found out that most of the junctions of inter-continent traces arelocated in a few countries.

关键词： Topology IP-level connectivity continent

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

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Computers, Materials & Continua 2019年第1期58卷 15-26页

作者： Menghua Luo Ke Wang Zhiping Cai Anfeng Liu Yangyang Li chak fong cheang College of Computer National University of Defense TechnologyChangsha410073China Normal College of Jishou University Jishou416000China School of Information Science and Engineering Central South UniversityChangsha410083China Innovation Center China Academy of Electronics and Information TechnologyBeijing100041China Faculty of Information Technology Macao University of Science and Technology519020Macao

The extreme imbalanced data problem is the core issue in anomaly *** amount of abnormal data is so small that we cannot get adequate information to analyze *** mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the *** the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning *** this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line *** our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data *** theory,our method covers a wider *** experiment with real world data,our method performs better than the SMOTE and its meliorations.

关键词： Anomaly detection imbalanced data synthetic data machine learning

Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

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Computers, Materials & Continua 2020年第2期62卷 917-927页

作者： Haiwen Chen Guang Yu Fang Liu Zhiping Cai Anfeng Liu Shuhui Chen Hongbin Huang chak fong cheang College of Computer Science National University of Defense TechnologyChangshaChina School of Data Science and Computer Science Sun Yat-sen UniversityGuangzhouChina College of Information and Engineering Central South UniversityChangshaChina Faculty of Information Technology Macao University of Science and TechnologyMacao

For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every *** to closely monitor various KPIs,and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge,especially for unlabeled *** generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are *** is a time-consuming and laborious work to label anomaly for company *** an unsupervised model to detect unlabeled data is an urgent need at *** this paper,unsupervised learning DBSCAN combined with feature extraction of data has been used,and for some KPIs,its best F-Score can reach about 0.9,which is quite good for solving the current problem.

关键词： Anomaly detection KPIs unsupervised learning algorithm