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China Communications 2019年第10期16卷 133-150页

作者： Juan Wang Shirong Hao Yi Li Zhi Hong Fei Yan Bo Zhao Jing Ma Huanguo Zhang Key Laboratory of Aerospace Information Security and Trust Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072HubeiChina School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China Science and Technology on Information Assurance Laboratory Beijing 100072China

network functions such as intrusion detection systems (IDS) have been increasingly deployed as virtual network functions or outsourced to cloud service providers so as to achieve the scalability and agility, and reducing equipment costs and operational cost. However, virtual intrusion detection systems (VIDS) face more serious security threats due to running in a shared and virtualized environment instead of proprietary devices. Cloud service providers or malicious tenants may illegally access and tamper with the policies, packet information, and internal processing states of intrusion detection systems, thereby violating the privacy and security of tenant’s networks. To address these challenges, we use Intel Software Guard Extensions (SGX) to build a Trusted Virtual Intrusion Detection System (TVIDS). For TVIDS, to prevent cloud service providers from accessing sensitive information about the users’ network, we build a trusted execution environment for security policy, packets processing, and internal state so that cloud service providers and other malicious tenants can’t access the protected code, policy, processing states, and packets information of the intrusion detection system. We implemented TVIDS on the basis of the Snort which is a famous open-source IDS and evaluated its results on real SGX *** results show that our method can protect the security of the virtual IDS and brings acceptable performance overhead.

关键词： network function virtualization intrusion detection system SGX trusted execution environment

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A Dynamic Collaborative Filtering Algorithm based on Convolutional Neural networks and Multi-layer Perceptron

A Dynamic Collaborative Filtering Algorithm based on Convolu...

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International Conference on Ubiquitous computing and Communications and International Symposium on Cyberspace and security (IUCC-CSS)

作者： Qiang Sun Lei-lei Shi Lu Liu Zi-xuan Han Liang Jiang Yao Lu John Panneerselvam School of Computer Science and Telecommunication Engineering Jiangsu University China Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace Jiangsu University China School of Computing and Mathematical Sciences University of Leicester UK School of Computer Jiangsu University of science and technology China

ISBN: (纸本)9781665466684

In the current information era of enormous amount of data generation, recommendation systems are increasingly used as an efficient way to mitigate information overload in networks. The recommendation systems are used to provide more focused and personalized recommendation of relevant items based on user's historical preferences and constraints. An efficient recommendation system should enrich and improve end user experience. At present, recommendation systems commonly use traditional collaborative filtering algorithms to learn the hidden vectors from the user's commodity behavior matrix. Most of the traditional algorithms characterize data sparseness and cold start problems. Whilst deep learning is being utilized to accurately learn the auxiliary information to enhance the recommendation provided by collaborative filtering techniques, the implementation of deep learning should be fine-tuned in such a way that is satisfies specific application contexts. This paper intends to build a deep collaborative filtering model that integrates Convolutional Neural networks and Multi-layer Perceptron, addressing the specific requirements of movie recommendation systems. Our proposed model can accurately capture the characteristics of local correlation and explicit and implicit feedback information. In the explicit recommendation task, implicit feedback search is used to embed similar groups, and multi-layer perceptron is used to model user projects, user similar project groups and project similar user groups. Experiments conducted based on the MovieLens dataset for predicting the movie scores demonstrate that our proposed algorithm improves the accuracy of movie score prediction, and outperforms notable traditional collaborative filtering algorithms in terms of alleviating the data sparseness and cold start problems.

关键词： Deep learning Collaborative filtering Heuristic algorithms Filtering algorithms Prediction algorithms Motion pictures Ubiquitous computing

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Sentiment Analysis of Web Text Based on Deep Learning with a Attention Mechanism

Sentiment Analysis of Web Text Based on Deep Learning with a...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Qing Yu Hui Zhao Kaiwen Jiang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

The sentiment analysis of online commentary is a hot research topic in the field of network information mining. Traditional text sentiment analysis methods are mainly based on emotional dictionaries or machine learning. However, it relies on the structure of the sentiment dictionary and the artificial design and extraction features, which limits the generalization ability. In contrast, the deep learning model has a stronger ability to express complex mapping functions from data to emotional semantics. This paper proposes a model combining Bidirectional Gated Recurrent Unit(BGRU) neural network and Convolutional Neural network(CNN), and introduces attention mechanism for text sentiment analysis. The experimental results show that compared with the traditional method, the method effectively improves the accuracy of text sentiment classification.

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ON THE OPPORTUNITIES OF GREEN computing: A SURVEY

arXiv

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arXiv 2023年

作者： Zhou, You Lin, Xiujing Zhang, Xiang Wang, Maolin Jiang, Gangwei Lu, Huakang Wu, Yupeng Zhang, Kai Yang, Zhe Wang, Kehang Sui, Yongduo Jia, Fengwei Tang, Zuoli Zhao, Yao Zhang, Hongxuan Yang, Tiannuo Chen, Weibo Mao, Yunong Li, Yi Bao, De Li, Yu Liao, Hongrui Liu, Ting Liu, Jingwen Guo, Jinchi Zhao, Xiangyu Wei, Ying Qian, Hong Liu, Qi Wang, Xiang Chan, Wai Kin Li, Chenliang Li, Yusen Yang, Shiyu Yan, Jining Mou, Chao Han, Shuai Jin, Wuxia Zhang, Guannan Zeng, Xiaodong Tsinghua University China Shanghai Jiaotong University China University of Science and Technology of China China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Huazhong University of Science and Technology China Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University China Xi’an Jiaotong University China College of Computer Science Nankai University China School of Computer Science China University of Geosciences China School of Information Science and Technology Beijing Forestry University China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration China Guangzhou University China Nanyang Technological University Singapore City University of Hong Kong Hong Kong Wuhan University China State Key Laboratory for Novel Software Technology Nanjing University China Ant Group China

Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers’ attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green computing. We propose the framework of Green computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon footprint

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Passive beamforming design for intelligent reflecting surface assisted MIMO systems

arXiv

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arXiv 2021年

作者： Feng, Chenghao Shen, Wenqian Gao, Xinyu An, Jianping School of Information and Electronics Beijing Institute of Technology Beijing100081 China Shaanxi Key Laboratory of Information Communication Network and Security Xian University of Posts and Telecommunications Shaanxi Xian710121 China Huawei Technologies Co. Ltd. Beijing100085 China

Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multiple-output (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks. © 2021, CC BY.

关键词： MIMO systems

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Velocity-Driven Particle Swarm Optimization 19

Velocity-Driven Particle Swarm Optimization

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Proceedings of the 2019 8th International Conference on computing and Pattern Recognition

作者： Wei Li Yaochi Fan Qiaoyong Jiang Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450376570

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. The performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. The historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. The experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems.

关键词： Evolutionary algorithms Velocity-driven strategy Particle swarm optimization Optimization problems

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Research on machine learning prediction of air quality index based on SPSS 3

Research on machine learning prediction of air quality index...

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3rd International Conference on Computer network, Electronic and Automation, ICCNEA 2020

作者： Ma, Limei Gao, Yijun Zhao, Chen College of Information Technology Hebei Normal University Shijiazhuang China School of Information Studies Dominican University River Forest United States Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China School of Information Studies Dominican University River Forest River ForestIN United States

ISBN: (纸本)9781728170831

Air From autumn to winter, air pollution in North China is very serious. If we can make effective prediction, we can achieve effective prevention. In this paper, SPSS is used to associate a phenomenon with multiple factors through the optimal combination of multiple independent variables. It is more effective and realistic to predict or estimate the dependent variables than to predict or estimate only one independent variable. Regression with two or more independent variables is multivariate linear regression, and multivariate linear regression has more practical significance than univariate linear regression °Through the establishment of multiple linear regression model, the factors affecting air quality were screened and analyzed. The factors influencing air quality index were PM2.5, PM10, SO2, NO2, CO and O3. Through regression analysis of one year's data samples, the prediction model is obtained. It has been proved that the prediction method is worth popularizing. © 2020 IEEE.

关键词： Forecasting

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A Semantic-based Replacement for Event Image Privacy

A Semantic-based Replacement for Event Image Privacy

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Zhenfei Chen Zhu Tianqing Bing Tian Yu Wang Wei Ren School of Computer Science China University of Geosciences Wuhan P.R. China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou P.R. China School of Computer Science China University of Geosciences Guangxi Key Laboratory of Cryptography and Information Security Key Laboratory of Network Assessment Technology CAS Beijing P.R. China

ISBN: (纸本)9781665416610

Thanks to the continuous development of deep learning and the updating of deep neural networks, the accuracy of various computer vision tasks continue improving. On the one hand, the accuracy of image recognition is significantly improved. On the other hand, it also poses a higher challenge of image-based privacy preservation. Although traditional privacy protection methods such as cryptography methods can provide a good privacy protection, they are extremely inconvenient to use and cannot provide good image utility. In order to obtain a balance between image privacy and utility, we propose a privacy-preserving model based on image semantic replacement. We perform semantic replacement or obfuscation to multiple information. Taking the human figures as an example, the information includes faces, scenes, and dressing style. As that information contributes the most to the recognition, we define those items as the privacy of the original image. We replace the event information of the original image, so that the figures in the image can no longer be recognized. With this strategy, the image can still be detected by various detection networks, such as scene detection, which ensures utility. The framework consists of three parts: detection network, scene replacement network, and clothing replacement network. A comprehensive and quantitative experiment set proves the effectiveness of the proposed model.

关键词： Deep learning Privacy Image recognition Scientific computing Semantics Clothing Neural networks

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Two-phases detection scheme: Detecting android malware in android markets

Two-phases detection scheme: Detecting android malware in an...

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International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2018

作者： Su, Xin Lin, Jiuchuan Shen, Fuhui Zheng, Yi Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Changsha China Department of Information Technology Hunan Police Academy Changsha China Key Lab of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China Zhejiang Economic Information Center Hangzhou China

ISBN: (纸本)9783319987750

Recently, Android application becomes popular and important in human’s daily work, life, entertainment. However, because of open source of Android application, more and more malware aim to this platform and launch various malicious attacks to threaten Android users’ security. Previous research works focus on using static behavioral analysis to detect Android malware, which cannot capture dynamic behaviors and in-efficiency to detect Android malware. In this paper, we present a Android application two-stage detection scheme that using two kinds of dynamic behavioral characteristics to detect Android malware. This framework first uses system call statistics to identify potential malicious apps. After verification, if the software is clean, the application will then be released to the relevant markets. To mitigate against false negative cases, users who run new apps can invoke our network traffic monitoring (NTM) tool which triggers network traffic capture upon detecting some suspicious behaviors e.g. detecting sensitive data being sent to output stream of an open socket. The network traffic will be analyzed to see if it matches network characteristics observed from malware apps. If suspicious network traffic is observed, the relevant Android markets will be notified to remove the application from the repository. We trained our system call and network traffic classifiers using 32 families of known Android malware families and some typical normal apps. Later, we evaluated our framework using other malware and normal apps that used in the training set. Our experimental results using 120 test apps (which consist of 50 malware and 70 normal apps) indicate that we can achieve a 94.2% and 99.2% accuracy with J.48 and Random forest classifier respectively using our framework. © Springer Nature Switzerland AG 2019.

关键词： Application programs

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OPEN: orthogonal propagation with ego-network modeling 22

OPEN: orthogonal propagation with ego-network modeling

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Liang Yang Lina Kang Qiuliang Zhang Mengzhe Li Bingxin Niu Dongxiao He Zhen Wang Chuan Wang Xiaochun Cao Yuanfang Guo School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) Northwestern Polytechnical University Xi'an China and School of Cybersecurity Northwestern Polytechnical University Xi'an China State Key Laboratory of Information Security IIE CAS Beijing China School of Cyber Science and Technology Sun Yat-sen University Shenzhen China School of Computer Science and Engineering Beihang University Beijing China and Zhongguancun Laboratory Beijing China

ISBN: (纸本)9781713871088

To alleviate the unfavorable effect of noisy topology in Graph Neural networks (GNNs), some efforts perform the local topology refinement through the pairwise propagation weight learning and the multi-channel extension. Unfortunately, most of them suffer a common and fatal drawback: irrelevant propagation to one node and in multi-channels. These two kinds of irrelevances make propagation weights in multi-channels free to be determined by the labeled data, and thus the GNNs are exposed to overfitting. To tackle this issue, a novel Orthogonal Propagation with Ego-network modeling (OPEN) is proposed by modeling relevances between propagations. Specifically, the relevance between propagations to one node is modeled by whole ego-network modeling, while the relevance between propagations in multi-channels is modeled via diversity requirement. By interpreting the propagations to one node from the perspective of dimension reduction, propagation weights are inferred from principal components of the ego-network, which are orthogonal to each other. Theoretical analysis and experimental evaluations reveal four attractive characteristics of OPEN as modeling high-order relationships beyond pairwise one, preventing overfitting, robustness, and high efficiency.

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