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Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study

Big Data Mining and Analytics 2018年第4期1卷 284-296页

作者： Chenxi Yang Yang Chen Qingyuan Gong Xinlei He Yu Xiao Yuhuan Huang Xiaoming Fu School of Computer Science Fudan UniversityShanghai 200433China Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of EducationShanghai 200433China Department of Communications and Networking Aalto University02150 EspooFinland Faculty of European Languages and Cultures Guangdong University of Foreign StudiesGuangzhou 510420China Institute of Computer Science University of Gottingen37077 GottingenGermany

Given that the USA and Germany are the most populous countries in North America and Western Europe,understanding the behavioral differences between American and German users of online social networks is *** this work,we conduct a data-driven study based on the Yelp Open *** demonstrate the behavioral characteristics of both American and German users from different aspects,i.e.,social connectivity,review styles,and spatiotemporal *** addition,we construct a classification model to accurately recognize American and German users according to the behavioral *** model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.

关键词： behavioral difference online social networks Yelp machine learning

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Distortion-free watermarking scheme for compressed data in columnar database 15

Distortion-free watermarking scheme for compressed data in c...

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15th International Joint Conference on e-Business and Telecommunications, ICETE 2018

作者： Yaqub, Waheeb Kamel, Ibrahim Aung, Zeyar Center for Cyber Security New York University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science Khalifa University of Science and Technology Masdar Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897583193

Digital watermarking is an effective technique for protecting various databases against data copyright infringement, piracy, and data tampering in the columnar database. Increasing deployments of various database systems and their versatile applications have raised the need for better watermarking schemes that are tailored to the target database systems’ specific architecture. Most of the existing digital watermarking schemes do not take into consideration the side effects that watermarking might have on the database’s important characteristics such as data compression and overall performance. In this research, we propose a distortion-free fragile watermarking scheme for columnar database architecture without interfering its underlying data compression scheme and its overall performance. The proposed scheme is flexible and can be adapted to various distributions of data. We tested our proposed scheme on both synthetic and real-world data, and proved its effectiveness. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Database systems

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A Modified Fuzzy Fingerprint Vault Based on Pair-Polar Minutiae Structures 13th

A Modified Fuzzy Fingerprint Vault Based on Pair-Polar Minut...

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13th International Conference on Information securityand cryptology, Inscrypt 2017

作者： Li, Xiangmin Ding, Ning Lu, Haining Gu, Dawu Wang, Shanshan Xu, Beibei Yuan, Yuan Yan, Siyun Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China School of Cyber Security Shanghai Jiao Tong University Shanghai China Information Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9783319751597

Biometrics-based cryptosystems are helpful tools facilitating our daily life. Examples are that people use their biometrics to identify themselves in clouds or protect passwords as well as other privacy information in mobile phones connected to Internet-of-Things. Among various biometrics, fingerprints are one used most widely. A promising type of secure cryptosystems based on fingerprints is the so called fuzzy fingerprint vaults which can bind a secret key to one fingerprint template and release the same key later with the help of a similar but not necessarily fully identical fingerprint template. However, some aspects such as the security and matching accuracy of existing fingerprint-based fuzzy vaults are still not satisfactory due to troublesome issues such as the alignment of fingerprints and minutiae quantization. In this paper we propose a modified fuzzy fingerprint vault based on pair-polar minutiae structures, aiming at achieving better performance. Recall that Li et al. (2016) presented a fuzzy vault based on pair-polar minutiae structures with two-level secure sketch in recovering the secret key. Unlike their scheme, we compress the two-level structure into one level in which minutia descriptors are exploited to match a vault and thus lower the time complexity of decoding. Our experiment evaluation shows that the proposed fuzzy fingerprint vault achieves high matching accuracy. Importantly, our vault is of strong security against existing attacks such as the brute-force attack, false-accept attack and correlation attack. Also this vault adopts the polynomial representation introduced by Dodis and Reyzin (2006) which is thus able to reduce the storage complexity. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Biometrics

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Stance-In-Depth Deep Neural Approach to Stance Classification

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Procedia computer science 2018年 132卷 1646-1653页

作者： Gayathri Rajendran Bhadrachalam Chitturi Prabaharan Poornachandran Dept. of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kerala India Amrita Center for Cyber Security & Networks

Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news detection etc.. The process of identifying stance of a user from the text is called stance detection. This article compares the headline and body pair of a news article and classifies the pair as related or unrelated. The related pair is further classified into agree, disagree, discuss. We call related as detailed classification and unrelated as broad classification. We employ deep neural nets for feature extraction and stance classification. RNN models and its extensions showed significant variations in the classification of detailed class. Bidirectional LSTM model achieved the best accuracy for broad as well as detailed classification.

关键词： Stance Classification deep learning RNN LSTM Gated Recurrent Unit (GRU) Bidirectional word2vec

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A blockchain-based architecture framework for secure sharing of personal health data 20

A blockchain-based architecture framework for secure sharing...

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20th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2018

作者： Amofa, Sandro Sifah, Emmanuel Boateng Obour Agyekum, Kwame O.-B. Abla, Smahi Xia, Qi Gee, James C. Gao, Jianbin School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Center for Cyber Security University of Electronic Science and Technology of China Chengdu China Department of Radiology University of Pennsylvania Philadelphia United States School of Resource and Enviroment Center for Digital Health University of Electronic Science and Technology China

ISBN: (纸本)9781538642948

Health information exchanges have been popular for some time with their advantages known and widely researched. In spite of their utility in increasing provider efficiency and decreasing administrative costs, one challenge that has persisted is the data owners inability to control data after transmission. The lack of technical mechanisms to effectively control patients' health data in the network significantly affects participation of health and medical institutions while perpetrating the silo-based data management that locks value and potential inherent in the data. This not only affects researchers due to the lack of data for research and analysis but the quality of life of *** present a blockchain-supported architectural framework for secure control of personal data in a health information exchange by pairing user-generated acceptable use policies with smart contracts. We highlight the merits of our system, its user-centric focus and also show experimental results along with directions for extending our work. The framework introduces minimal risk to data by architecting a mechanism for controlling data after sharing. In adopting our framework, health service providers can deliver a stronger assurance for data management than is possible with current systems. © 2018 IEEE.

关键词： Blockchain

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Targeted Malicious Email Detection using Hypervisor-based Dynamic Analysis and Ensemble Learning

Targeted Malicious Email Detection using Hypervisor-based Dy...

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IEEE Global Communications Conference

作者： Jian Zhang Zhaojun Gu Wenzhen Li Liangyi Gong Jeffrey Wu School of Computer Science and Engineering Tianjin University of Technology College of Cyber Science Nankai University Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Software and BNRist Tsinghua University Beijing China Information Security Evaluation Center of Civil Aviation Civil Aviation University of China Tianjin China Jiaxing Chenxiang Information Technology Co. Ltd. Jiaxing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

At present, email is still one of the most frequently used communication tools for organizations and individuals. With the leakage of personal privacy information, targeted malicious email (TME) is becoming a prominent targeted cyber attack vector in today's Internet. This type of attack often uses personal information, about an individual, group of individuals, or an organization, to make a TME more believable and personalized. TME is effective to penetrate email defense system because it is fundamentally difficult for traditional email security method to distinguish legitimate emails from malicious emails. And TMEs often contain malicious URLs or malicious attachments, which are extremely aggressive and destructive. In order to effectively deal with this new type of malicious email attack, this paper proposes a dynamic detection method for malicious email. We simulate the recipient opening the email in the virtual machine (VM), accessing the URL and activating the attachment. And we use the virtual machine introspection (VMI) and memory forensics analysis (MFA) technology to obtain the dynamic features of the email by the out-of-VM. Then we use AdaBoostM1 ensemble learning method and Voting combination strategy to combine three base classifiers such as BayesNet, SMO and J48 to build a powerful classification model for detecting TME attacks. The AdaBoostM1 classifier achieved the high detection rates, with an AUC of 0.997, true positive rate (TPR) of 0.997, and false positive rate (FPR) of 0.015. In addition, our proposed detection method is superior to the 56 anti-virus engines on VirusTotal and most of the existing research works.

关键词： Electronic mail Feature extraction Virtual machining Unsolicited e-mail Uniform resource locators Learning systems

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Towards the Trust-Enhancements of Single Sign-On Services

Towards the Trust-Enhancements of Single Sign-On Services

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IEEE Conference on Dependable and Secure Computing

作者： Xuhua Bao Xiaokun Zhang Jingqiang Lin Dawei Chu Qiongxiao Wang Fengjun Li Legendsec Information Technology (Beijing) Inc. CHINA Joint Laboratory of Institute of Information Engineering Chinese Academy of Sciences and QiAnXin Group Academy of Opto-Electronics Chinese Academy of Sciences State Key Laboratory of Information Security Chinese Academy of Sciences Data Assurance and Communication Security Research Center Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Department of Electrical Engineering and Computer Science University of Kansas USA

Single sign-on (SSO) becomes popular as the identity management and authentication infrastructure in the Internet. A user receives an SSO ticket after being authenticated by the identity provider (IdP), and this IdP-issued ticket enables him to sign onto the relying party (RP). However, there are vulnerabilities (e.g., Golden SAML) that allow attackers to arbitrarily issue SSO tickets and then sign onto any RP on behalf of any user. Meanwhile, several incidents of certification authorities (CAs) also indicate that the trusted third party of security services is not so trustworthy as expected, and fraudulent TLS server certificates are signed by compromised or deceived CAs to launch TLS man-in-the-middle attacks. Various approaches are then proposed to tame the absolute authority of (compromised) CAs, to detect or prevent fraudulent TLS server certificates in the TLS handshakes. The trust model of SSO services is similar to that of certificate services. So this paper investigates the defense strategies of these trust-enhancements of certificate services, and attempts to apply these strategies to SSO to derive the trust-enhancements applicable in the SSO services. Our analysis derives (a) some security designs which have been commonly-used in the SSO services or non-SSO authentication services, and (b) two schemes effectively improving the trustworthiness of SSO services, which are not widely discussed or adopted.

关键词： Servers Public key Browsers Authentication Authorization Pins

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On the hardness of learning parity with noise over rings 12th

On the hardness of learning parity with noise over rings

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12th International Conference on Provable security, ProvSec 2018

作者： Zhao, Shuoyao Yu, Yu Zhang, Jiang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Laboratory for Wireless Security Xi’an University of Posts and Telecommunications Xi’an China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China Westone Cryptologic Research Center Beijing100070 China

ISBN: (纸本)9783030014452

Learning Parity with Noise (LPN) represents a notoriously hard problem in learning theory and it is also closely related to the "decoding random linear codes" problem in coding theory. Recently LPN has found many cryptographic applications such as authentication protocols, pseudorandom generators/functions and even advanced tasks including public-key encryption (PKE) schemes and oblivious transfer (OT) protocols. Crypto-systems based on LPN are computationally efficient and parallelizable in concept, thanks to the simple algebraic structure of LPN, but they (especially the public-key ones) are typically inefficient in terms of public-key/ciphertext sizes and/or communication complexity. To mitigate the issue, Heyse et al. (FSE 2012) introduced the ring variant of LPN (Ring-LPN) that enjoys a compact structure and gives rise to significantly more efficient cryptographic schemes. However, unlike its large-modulus analogue Ring-LWE (to which a reduction from ideal lattice problems can be established), no formal asymptotic studies are known for the security of Ring-LPN or its connections to other hardness assumptions. Informally, we show that for (Formula presented) and (Formula presented): assume that the decisional LPN problem of noise rate μ is hard even when its matrix is generated by a random Ring-LPN instance of noise rate μ′ (whose matrix is also kept secret in addition to secret and noise), then either Ring-LPN of noise rate δ is hard or public-key cryptography is implied. We remark that the heuristic-based approach to public randomness generation (as used in the assumption) is widely adopted in practice, and the latter statement is less likely because noise rate (Formula presented) is believed to reside in the minicrypt-only regime for LPN. Therefore, our results constitute non-trivial evidence that Ring-LPN might be as hard as LPN. © Springer Nature Switzerland AG 2018.

关键词： Hardness

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NTIRE 2021 challenge on video super-resolution

arXiv

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arXiv 2021年

作者： Son, Sanghyun Lee, Suyoung Nah, Seungjun Timofte, Radu Lee, Kyoung Mu Chan, Kelvin C.K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Jiang, Boyuan Lin, Chuming Dong, Yuchun Luo, Donghao Chu, Wenqing Ji, Xiaozhong Yang, Siqian Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Chengpeng Chu, Xiaojie Zhang, Jie Lu, Xin Chen, Liangyu Lin, Jing Du, Guodong Hao, Jia Zou, Xueyi Zhang, Qi Jiang, Lielin Li, Xin Zheng, He Liu, Fanglong He, Dongliang Li, Fu Dang, Qingqing Yi, Peng Wang, Zhongyuan Jiang, Kui Jiang, Junjun Ma, Jiayi Chen, Yuxiang Wang, Yutong Liu, Ting Sun, Qichao Liang, Huanwei Li, Yiming Li, Zekun Ruan, Zhubo Shang, Fanjie Guo, Chen Li, Haining Luo, Renjun Shen, Longjie Zafirouli, Kassiani Karageorgos, Konstantinos Konstantoudakis, Konstantinos Dimou, Anastasios Daras, Petros Song, Xiaowei Zhuo, Xu Liu, Hanxi Guo, Mengxi Li, Junlin Li, Yu Zhu, Ye Wang, Qing Zhao, Shijie Sun, Xiaopeng Zhan, Gen Xie, Tangxin Jia, Yu Lu, Yunhua Zhang, Wenhao Sun, Mengdi Michelini, Pablo Navarrete Zhang, Xueheng Jiang, Hao Chen, Zhiyu Chen, Li Xiong, Zhiwei Xiao, Zeyu Xu, Ruikang Cheng, Zhen Fu, Xueyang Song, Fenglong Luo, Zhipeng Yao, Yuehan Dutta, Saikat Shah, Nisarg A. Das, Sourya Dipta Zhao, Peng Shi, Yukai Liu, Hongying Shang, Fanhua Liu, Yuanyuan Chen, Fei Yu, Fangxu Gao, Ruisheng Bai, Yixin Heo, Jeonghwan Yue, Shijie Li, Chenghua Li, Jinjing Zheng, Qian Gang, Ruipeng Song, Ruixia Wee, Seungwoo Jeong, Jechang Li, Chen Wen, Geyingjie Chai, Xinning Song, Li Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland S-Lab Nanyang Technological University Singapore YouTu Lab Tencent East China Normal University China Megvii Peking University China Fudan University China Noah's Ark Lab Huawei Canada Technologies Co. Ltd Baidu Inc National Engineering Research Center for Multimedia Software Wuhan University China School of Computer Science and Technology Harbin Institute of Technology China Electronic Information School Wuhan University China School of Computer Science and Engineering University of Electronic Science and Technology of China China MTlab Meitu Inc Xidian university China ITI CERTH Greece Southeast University China Tencent PCG South China University of Technology China ByteDance Sun Yat-Sen University China Electronic Technology Cyber Security Co. Ltd. AIoT R&D Centre BOE Technology Group Co. Ltd. Shanghai Jiao Tong University China University of Science and Technology of China China Huawei Noah's Ark Lab DeepBlue Technology Shanghai Co. Ltd Indian Institute of Technology Madras India Indian Institute of Technology Jodhpur India Jadavpur University India Xidian University China Hanyang University Korea Republic of NCUT China CASIA China CUC China NRTA China

Super-Resolution (SR) is a fundamental computer vision task that aims to obtain a high-resolution clean image from the given low-resolution counterpart. This paper reviews the NTIRE 2021 Challenge on Video Super-Resolution. We present evaluation results from two competition tracks as well as the proposed solutions. Track 1 aims to develop conventional video SR methods focusing on the restoration quality. Track 2 assumes a more challenging environment with lower frame rates, casting spatio-temporal SR problem. In each competition, 247 and 223 participants have registered, respectively. During the final testing phase, 14 teams competed in each track to achieve state-of-the-art performance on video SR tasks. © 2021, CC BY-NC-ND.

关键词： Optical resolving power

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Password Guessing Time Based on Guessing Entropy and Long-Tailed Password Distribution in the Large-Scale Password Dataset 11

Password Guessing Time Based on Guessing Entropy and Long-Ta...

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2017 11th IEEE International Conference on Anti-counterfeiting,security,and Identification(ASID)

作者： Shuo Zhang Jianping Zeng Zewen Zhang School of Computer Science Fudan University Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education

ISBN: (纸本)9781538605349;9781538605332

Currently the password security is serious, but there is not an appropriate metric for measuring passwords. Thus, the main purpose of this paper is to provide a security time period for the user's password in an online system, allowing the user to modify the password before the security period arrives to prevent the attacker from guessing correctly. We use the guessing chain to calculate the expected time that the attacker need to guess the target password correctly based on the guessing entropy. We assume that the attacker uses a dictionary attack, which is also a probability sequence, and the dictionary is non-ordered or ordered. At the same time, we analyze the large-scale password dataset Rockyou, which contains nearly 32 million passwords. And we assume that the ordered dictionary is organized in descending frequency, in which the probability of the occurrence of the password obeys a long-tailed distribution. We explore the form of the distribution function. And we first find that the simple Zipf distribution can better fit with the empirical distribution of ordered dictionary.

关键词： password security guessing chain guessing entropy long-tailed distributions

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