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Corrigendum to “COIN: A fast packet inspection method over compressed traffic” [J. Netw. Comput. Appl. 127 (2019) 122–134]

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Journal of network and computer Applications 2020年 153卷

作者： Xiuwen Sun Hao Li Dan Zhao Xingxing Lu Kaiyu Hou Chengchen Hu Ministry of Education Key Lab for Intelligent Network and Network Security Department of Computer Science and Technology Xi'an Jiaotong University China Xi'an University of Finance and Economics China Department of Electrical Engineering and Computer Science Northwestern University USA

Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks

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arXiv 2020年

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Bai, Bo State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an Shaanxi710071 China Science and Technology on Communication Network Laboratory Shijiazhuang Hebei050081 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai200050 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China 2012 Labs Huawei Technologies Co. Ltd. Hong Kong Hong Kong

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Code Authority Control Method Based on File security Level and ACL in WebIDE

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Code Authority Control Method Based on File Security Level a...

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Junhuai Li Wenchao Ji Jubo Tian Huaijun Wang Kan Wang School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

The following topics are dealt with: mobile robots; learning (artificial intelligence); control system synthesis; permanent magnet motors; motion control; optimisation; power engineering computing; machine control; sy... 详细信息

ISBN: (纸本)9781538674178

关键词： Programming Authorization Performance evaluation Privacy Task analysis

On privacy protection of latent dirichlet allocation model training

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arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

Revisiting Cloud Migration: Strategies and Methods

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Wendong Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China Yan'an University Xi'an 716000 China

Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

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Exploiting Meta-Path with Attention Mechanism for Fine-Grain...

2019 3rd International Conference on Data Mining, Communications and Information technology (DMCIT 2019)

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology Telematics Group the University of Goettingen Shaanxi Key Laboratory of Network Computing and Security School of Electronic and Information Engineering Xi'an Jiaotong University College of Xi'an Innovation Yan'an University Yan'an University National Satellite Meteorological Center China Meteorological Administration

Based on the huge volumes of user check-in data in LBSNs,users’ intrinsic mobility patterns can be well explored,which is fundamental for predicting where a user will visit next given his/her historical check-in *** there are various types of nodes and interactions in LBSNs,they can be treated as Heterogeneous Information network（HIN） where multiple semantic meta-paths can be *** by the recent success of meta-path context based embedding techniques in HIN,in this paper,we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location *** results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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A Universal Designated Multi-Verifier Transitive Signature Scheme 13th

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A Universal Designated Multi-Verifier Transitive Signature S...

13th International Conference on Information securityand Cryptology, Inscrypt 2017

作者： Zhu, Fei Zhang, Yuexin Lin, Chao Wu, Wei Meng, Ru Fujian Provincial Key Laboratory of Network Security and Cryptology School of Mathematics and Informatics Fujian Normal University Fuzhou China School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC3122 Australia School of Computer Science Shaanxi Normal University Xi’an China

ISBN: (纸本)9783319751597

A Universal Designated Verifier Transitive Signature (UDVTS) scheme is designed for the graph-based big data system. Specifically, it allows a transitive signature holder to convince the designated verifier with a transitive signature. Nevertheless, existing UDVTS schemes cannot be directly employed in the scenarios when multi-verifier are involved. Thus, in this paper, we extend the notion to the Universal Designated Multi-Verifier Transitive Signature (UDMVTS) scheme. Namely, our new scheme allows a transitive signature holder to designate the signature to multi-verifier. Furthermore, for the proposed scheme, we formalize its security notions and prove its security in the random oracle model. We also analyse the performance of our scheme to demonstrate its efficiency. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Graphic methods

LPD communication: A sequential change-point detection perspective

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arXiv 2019年

作者： Huang, Ke-Wen Wang, Hui-Ming Towsley, Don Vincent Poor, H. School of Electronics and Information Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaanxi710049 China College of Information and Computer Sciences University of Massachusetts AmherstMA01002 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

In this paper, we establish a framework for low probability of detection (LPD) communication from a sequential change-point detection (SCPD) perspective, where a transmitter, Alice, wants to hide her signal transmission to a receiver, Bob, under the surveillance of an adversary, Willie. The new framework facilitates to model LPD communication and further evaluate its performance under the condition that Willie has no prior knowledge on when the transmission from Alice starts and that Willie wants to detect the existence of the communication as quickly as possible in real-time manner. We consider three different sequential tests for Willie, i.e., the Shewhart test, the cumulative sum (CUSUM) test, and the Shiryaev-Roberts (SR) test, to model the detection procedure. Communication is said to be covert if it stops before detection by Willie with high probability. Covert probability defined as the probability that Willie is not alerted during the communication procedure is investigated. We formulate an optimization problem aimed at finding the transmit power and transmission duration such that the total amount of information that can be transmitted is maximized subject to a high covert probability. Under Shewhart test, closed-form approximations of the optimal transmit power and transmission duration are derived, which well approximate the solutions obtained from exhaustive search. As for CUSUM and SR tests, we provide an effective algorithm to search the optimal solution. Numeric results are presented to show the performance of LPD communication. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability

Time-varying massive MIMO channel estimation: Capturing, reconstruction and restoration

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arXiv 2019年

作者： Li, Muye Zhang, Shun Zhao, Nan Zhang, Weile Wang, Xianbin State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Information and Communication Engineering Dalian University of Technology Dalian116024 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Engineering Western University LondonON Canada

On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： MIMO systems