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IEEE International Conference on Automated Software Engineering (ASE)

作者： Yueming Wu Xiaodi Li Deqing Zou Wei Yang Xin Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab National Engineering Research Center for Big Data Technology and System School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Texas at Dallas Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

关键词： Malware Social networking (online) Feature extraction Semantics Analysis of variance Static analysis Robustness

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Risk evaluation of financial websites based on structure mining

Risk evaluation of financial websites based on structure min...

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International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017

作者： Li, Huakang Dai, Yuhao Jin, Xu Sun, Guozi Li, Tao Xu, Zheng Jiangsu Key Lab of Big Data and Security and Intelligent Processing School of Computer Science and Technology School of Software Nanjing University of Posts and Telecommunications Nanjing China The Third Research Institute of the Ministry of Public Security Shanghai China

ISBN: (纸本)9783319670706

With the development of network communication and security authentication technologies, Internet finance, a new financial business model which allows customers to achieve online financing, payment, investment and lending, becomes more and more popular. Risk of internet finance is much higher than that of traditional financial system because of the rapid fund flowing, lack of personal credit audit, deficiency of standard network operation and imperfect of information security. Current risk control of Internet finance mainly contains network security, financial self-discipline, investor education and governmental regulatory. In this paper, we propose a risk evaluation method based on structure mining for financial website after analyzing a large number of Internet financial sites. The kernel functions algorithm of natural language syntax tree is introduced to classify the security level of website. While the URL is considered as a long sentence and the path segments are defined as keywords. The experimental results demonstrate that the structure mining method can simply evaluate the risk of Internet financial website to achieve acceptable accuracy. © 2018, Springer International Publishing AG.

关键词： Websites

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Early anomaly detection in wind turbine bolts breaking problem — Methodology and application

Early anomaly detection in wind turbine bolts breaking probl...

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IEEE International Conference on big data Analysis (ICBDA)

作者： Chung-Wei Wu Mei Chen Big Data Science Lab Neucloud Technology Co. Beijing Ltd. Beijing China

Early anomaly detection plays an important role in many fields, such as fraud detection in financial data, a signal indicating machine unhealthy status, etc. It leads to fault diagnostics and even prognostics according to data analytics need. In this paper, we apply an early anomaly detection model in wind turbine bolts breaking problem with good detection result, which can be used to substitute prognostic model in a solid manner. Wind turbine bolts breaking is a common problem in wind turbine, which may cause serious impact but hard to detect, especially when no extra sensor on total 50 bolts for each blade. To solve this problem, a novel process is designed to answer following questions: (1) whether there are parameters whose behavior is influenced by the bolts breaking, (2) can we detect such parameters reflecting the breaking problem, and (3) find out how the impacts happen and how far the impacts grow by comparing different number of blots are broken. We develop a conditional detection model, which is able to detect only 2 bolts breaking by automatically extracting key features with 92% accuracy.

关键词： Fasteners Wind turbines Anomaly detection data models Maintenance engineering Support vector machines Wind speed

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Frontline Health Care Workers' Mental Health and Well-Being During the First Year of the COVID-19 Pandemic: Analysis of Interviews and Social Media data

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Journal of Medical Internet Research 2023年第1期25卷 e43000页

作者： Juan, Norha Vera San Martin, Sam Badley, Anna Maio, Laura Gronholm, Petra C. Buck, Caroline Flores, Elaine C. Vanderslott, Samantha Syversen, Aron Symmons, Sophie Mulcahy Uddin, Inayah Karia, Amelia Iqbal, Syka Vindrola-Padros, Cecilia Rapid Research Evaluation and Appraisal Lab (RREAL) Department of Targeted Intervention University College London London United Kingdom Centre for Global Mental Health and Centre for Implementation Science Health Services and Population Research Department Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Ethox Centre Big Data Institute University of Oxford Oxford United Kingdom Academy Research and Improvement Southampton United Kingdom School of Health Sciences University of Southampton Southampton United Kingdom Department of Behavioural Science and Health University College London London United Kingdom Centre on Climate Change & Planetary Health London School of Hygiene and Tropical Medicine London United Kingdom Stanford Center for Innovation in Global Health Stanford Woods Institute for the Environment Stanford University Stanford CA United States Oxford Vaccine Group Churchill Hospital University of Oxford Oxford United Kingdom NIHR Oxford Biomedical Research Centre University of Oxford Oxford United Kingdom Institute of Epidemiology and Healthcare University College London London United Kingdom Centre for Interdisciplinary Research Education and Innovation in Health Systems School of Nursing Midwifery and Health Systems University College Dublin Dublin Ireland Division of Psychiatry Marie Curie Palliative Care Research Department University College London London United Kingdom Department of Psychology University of Bradford Bradford United Kingdom

BACKGROUND: The COVID-19 pandemic has shed light on fractures in health care systems worldwide and continues to have a significant impact, particularly in relation to the health care workforce. Frontline staff have been exposed to unprecedented strain, and delivering care during the pandemic has affected their safety, mental health, and well-being. OBJECTIVE: This study aimed to explore the experiences of health care workers (HCWs) delivering care in the United Kingdom during the COVID-19 pandemic to understand their well-being needs, experiences, and strategies used to maintain well-being (at individual and organizational levels). METHODS: We analyzed 94 telephone interviews with HCWs and 2000 tweets about HCWs' mental health during the first year of the COVID-19 pandemic. RESULTS: The results were grouped under 6 themes: redeployment, clinical work, and sense of duty;well-being support and HCW's coping strategies;negative mental health effects;organizational support;social network and support;and public and government support. CONCLUSIONS: These findings demonstrate the need for open conversations, where staff's well-being needs and the strategies they adopted can be shared and encouraged, rather than implementing top-down psychological interventions alone. At the macro level, the findings also highlighted the impact on HCW's well-being of public and government support as well as the need to ensure protection through personal protective equipment, testing, and vaccines for frontline workers. ©Norha Vera San Juan, Sam Martin, Anna Badley, Laura Maio, Petra C Gronholm, Caroline Buck, Elaine C Flores, Samantha Vanderslott, Aron Syversen, Sophie Mulcahy Symmons, Inayah Uddin, Amelia Karia, Syka Iqbal, Cecilia Vindrola-Padros. Originally published in the Journal of Medical Internet Research (https://***), 14.08.2023.

关键词： Collaborative and Digital Analysis of big Qualitative data in Time Sensitive Contexts COVID-19 frontline health care workers health services research LISTEN method mental health

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Forward and backward information retention for accurate binary neural networks

arXiv

引用

arXiv 2019年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Shen, Mingzhu Wei, Ziran Yu, Fengwei Song, Jingkuan State Key Lab of Software Development Environment Beihang University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University SenseTime Research Beijing University of Posts and Telecommunications Center for Future Media University of Electronic Science and Technology of China

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation;(2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Saudi cloud infrastructure: a security analysis

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science China(Information sciences) 2017年第12期60卷 152-164页

作者： Wahid RAJEH Hai JIN Deqing ZOU Services Computing Technology and System Lab Big Data Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology School of Computers and Information Technology University of Tabuk

The growing demand and dependence upon cloud services have garnered an increasing level of threat to user data and security. Some of such critical web and cloud platforms have become constant targets for persistent malicious attacks that attempt to breach security protocol and access user data and information in an unauthorized manner. While some of such security compromises may result from insider data and access leaks, a substantial proportion continues to remain attributed to security flaws that may exist within the core web technologies with which such critical infrastructure and services are developed. This paper explores the direct impact and significance of security in the Software Development Life Cycle(SDLC) through a case study that covers some 70 public domain web and cloud platforms within Saudi Arabia. Additionally, the major sources of security vulnerabilities within the target platforms as well as the major factors that drive and influence them are presented and discussed through experimental evaluation. The paper reports some of the core sources of security flaws within such critical infrastructure by implementation with automated security auditing and manual static code analysis. The work also proposes some effective approaches, both automated and manual, through which security can be ensured through-out the SDLC and safeguard user data integrity within the cloud.

关键词： cloud security vulnerability detection web security Saudi infrastructure cloud service

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Matching Biomedical Ontologies with Long Short-Term Memory Networks

Matching Biomedical Ontologies with Long Short-Term Memory N...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chao Jiang Xingsi Xue Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian China School of Computer Science and Mathematics Fujian University of Technology Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin Guangxi China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Semantic Web (SW) has attracted the increasing attention of researchers, which facilitates people to link and handle various data. Ontology is the kernel technique of SW, and biomedical ontology is a state-of-art biomedical knowledge modeling technique, which formally defines the biomedical concepts and their relationships. However, the same biomedical concepts in different biomedical ontologies could be defined in various contexts or with different terms, which yields the biomedical ontology heterogeneity problem. It is crucial to find mapping among heterogeneity concepts of different biomedical ontologies for bridging the semantic gaps, which is the so-called biomedical ontology matching. Biomedical ontology matching problem is an open challenge due to the rich semantic meaning and the flexible representation on a biomedical concept. To address this challenging problem, in this work, it is regarded as a binary classification problem, and a Long Short-Term Memory Networks (LSTM)-based ontology matching technique is proposed to solve it. Our proposal improves the quality of the alignment by introducing the char-embedding technique, which takes into account the semantic and context information of concepts. The comparing results with OAEI's participants show the effectiveness of our proposal.

关键词： Ontologies Semantics Logic gates Training Human anatomy Proposals Biomedical measurement

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Coevolution spreading in complex networks

arXiv

引用

arXiv 2019年

作者： Wang, Wei Liu, Quan-Hui Liang, Junhao Hu, Yanqing Zhou, Tao Cybersecurity Research Institute Sichuan University Chengdu610065 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610054 China Compleχ Lab University of Electronic Science and Technology of China Chengdu610054 China College of Computer Science Sichuan University Chengdu610065 China School of Mathematics Sun Yat-Sen University Guangzhou510275 China School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China

The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Complex networks

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Matching Biomedical Ontologies Through Compact Evolutionary Simulated Annealing Algorithm 12th

Matching Biomedical Ontologies Through Compact Evolutionary ...

引用

12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

作者： Xue, Xingsi Chen, Jie Liu, Jianhua Chen, Dongxu College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fujian350118 China Fujian Medical University Union Hospital Fuzhou Fujian350001 China

ISBN: (纸本)9789811358401

To overcome biomedical ontology’s heterogeneous problem, biomedical ontology matchers are developed to search for bridges of knowledge (or alignment) between heterogeneous biomedical ontologies. Since it is a complex problem, Evolutionary Algorithm (EA) can present a good methodology for matching biomedical ontologies. To improve the efficiency, in this paper, a Compact Evolutionary Simulated Annealing algorithm (CESA) is proposed to match the biomedical ontologies. In particular, CESA utilizes a Probability Vector (PV) to simulate the behavior of population-based EA, and introduces the Simulated Annealing algorithm (SA) as a local search in each generation. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show the effectiveness of our proposal. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Simulated annealing

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