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2017 NATO IST-153/RWS-21 Workshop on Cyber Resilience, IST-153 2017

作者： Alexeev, Alexander Henshel, Diane S. Levitt, Karl McDaniel, Patrick Rivera, Brian Templeton, Steven Weisman, Mike Data Analysis and Modeling Indiana University School of Public and Environmental Affairs 1315 E 10th Street BlooomingtonIN47405 Indiana University School of Public and Environmental Affairs 1315 E 10th Street BloomingtonIN47405 Computer Science College of Engineering University of California Davis 1 Shields Ave. DavisCA05616 School of Electrical Engineering and Computer Science Pennsylvania State University Information Sciences and Technology Building University ParkPA16802-6823 Network Science Division Tactical Network Assurance Branch U.S. Army Research Laboratory 2800 Powder Mill Road AdelphiMD20783 Computer Security Research Lab Department of Computer Science University of California Davis One Shields Avenue DavisCA95616 Network Science Division Network Security Branch U.S. Army Research Laboratory ICF International 2800 Powder Mill Road AdelphiMD20783

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Three Layer Game Theoretic Decision Framework for Cyber-Investment and Cyber-Insurance 8th

Three Layer Game Theoretic Decision Framework for Cyber-Inve...

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8th International Conference on Decision and Game Theory for security, GameSec 2017

作者： Tosh, Deepak K. Vakilinia, Iman Shetty, Sachin Sengupta, Shamik Kamhoua, Charles A. Njilla, Laurent Kwiat, Kevin Department of Computer Science Norfolk State University NorfolkVA United States Department of Computer Science and Engineering University of Nevada RenoNV United States Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Network Security Branch Army Research Laboratory AdelphiMD United States Cyber Assurance Branch Air Force Research Laboratory RomeNY United States

ISBN: (纸本)9783319687100

Cyber-threat landscape has become highly complex, due to which isolated attempts to understand, detect, and resolve cybersecurity issues are not feasible in making a time constrained decisions. Introduction of cyber-threat information (CTI) sharing has potential to handle this issue to some extent, where knowledge about security incidents is gathered, exchanged across organizations for deriving useful information regarding the threat actors and vulnerabilities. Although, sharing security information could allow organizations to make informed decision, it may not completely eliminate the risks. Therefore, organizations are also inclined toward considering cyber-insurance for transferring risks to the insurers. Also, in networked environment, adversaries may exploit the information sharing to successfully breach the participating organizations. In this paper, we consider these players, i.e. organizations, adversary, and insure, to model a three layer game, where players play sequentially to find out their optimal strategies. Organizations determine their optimal self-defense investment to make while participating in CTI sharing and cyber-insurance. The adversary looks for an optimal attack rate while the insurer targets to maximize its profit by offering suitable coverage level to the organizations. Using backward induction approach, we conduct subgame perfect equilibrium analysis to find optimal strategies for the involved players. We observe that when cyber-insurance is not considered, attacker prefers to increase its rate of attack. This motivates the organizations to consider cyber-insurance option for transferring the risks on their critical assets. © 2017, Springer International Publishing AG.

关键词： Game theory

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

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Journal of Physics: Conference Series 2019年第1期1284卷

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 710048 Xi'an China Telematics Group the University of Goettingen 37077 Goettingen Germany Shaanxi Key Laboratory of Network Computing and Security 710048 Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University 710048 Xi'an China College of Xi'an Innovation Yan'an University 710100 Xi'an China Yan'an University Yan'an 716000 China National Satellite Meteorological Center China Meteorological Administration Beijing 100081 China

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 records. As 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 extracted. Inspired 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 prediction. Experimental 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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Knowledge graph embedding with iterative guidance from soft rules

arXiv

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arXiv 2017年

作者： Guo, Shu Wang, Quan Wang, Lihong Wang, Bin Guo, Li Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences State Key Laboratory of Information Security Chinese Academy of Sciences National Computer Network Emergency Response Technical Team & Coordination Center of China

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines;and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://***/iieir-km/RUGE. Copyright © 2017, The Authors. All rights reserved.

关键词： Knowledge graph

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Achieve Personalized Anonymity Through Query Blocks Exchanging

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China Communications 2016年第11期13卷 106-118页

作者： Chunguang Ma Lei Zhang Songtao Yang Xiaodong Zheng Pinhui Ke College of Computer Science and Technology Harbin Engineering University Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University College of Information and Electronic Technology Jiamusi University College of Applied Technology Qiqihar University

In cyberspace security,the privacy in location-based services(LBSs) becomes more critical. In previous solutions,a trusted third party(TTP) was usually employed to provide disturbance or obfuscation,but it may become the single point of failure or service bottleneck. In order to cope with this drawback,we focus on another important class,establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms,we found users in the group must share the same maximum anonymity degree,and they could not ease the process of preservation in a lower one. To cope with this problem,we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms,and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally,experimental evaluations further verify its effectiveness and efficiency.

关键词： cyberspace security location-based services collaborative users personalized anonymity query and location privacy

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Detecting Adversaries in Distributed Estimation

Detecting Adversaries in Distributed Estimation

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Asilomar Conference on Signals, Systems, and computers

作者： Yuan Chen Soummya Kar Jose M. F. Moura School of Information Engineering Zhengzhou University Zhengzhou China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Southampton Wireless School of Electronics and Computer Science University of Southampton Southampton U.K.

This paper studies distributed parameter estimation in the presence of adversarial agents. We consider the Flag Raising Distributed Estimation (FRDE) algorithm, a consensus+innovations distributed algorithm for the non-compromised agents to simultaneously perform parameter estimation and detect the presence of adversaries. So long as the non-compromised agents form a connected network and are globally observable, then, we can show that the FRDE algorithm either leads the non-compromised agents to correctly estimate the parameter or detect the adversarial activity preventing correct estimation. We demonstrate the performance of the FRDE algorithm through numerical examples.

关键词： Estimation Sensors network topology Distributed algorithms Pollution measurement Topology Parameter estimation

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INTELLIGENT INFORMATION network security AND MANAGEMENT

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China Communications 2016年第7期13卷 3-6页

作者： Yixian Yang Zhongshan Zhang Wenjing Li Information Security Center(ISC) Beijing University of Posts and Telecommunications(BUPT) National Engineering Laboratory for Disaster Backup and Recovery the School of Computer and Communication Engineering in the University of Science and Technology Beijing(USTB) State Key Lab of Networking and Switching Technologies Network Management Research Center at BUPT National Innovation Research Group of China

Intelligent Information network security and Management(IINSM)is an emerging direction in future Internet architecture research,which has gained significant tractions among academia and *** the rapid development of modern society,intelligent information network with the computer as the core technology is widely used in every aspect of social ***

关键词： DCT INTELLIGENT INFORMATION network security AND MANAGEMENT Simulation DWT DCNN

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Out-of-band discovery and evaluation for Tor hidden services 16

Out-of-band discovery and evaluation for Tor hidden services

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Li, Kang Liu, Peipeng Tan, Qingfeng Shi, Jinqiao Gao, Yue Wang, Xuebin Institute of Information Engineering CAS China National Engineering Laboratory for Information Security Technologies China University of Chinese Academy of Sciences China National Computer Network Emergency Response Technical Team Coordination Center China Graduated University CAS China Institute of Information Engineering CAS China National Engineering Laboratory for Information Security Technologies China

ISBN: (纸本)9781450337397

Tor is one of the most well-known anonymity networks that provides anonymity to service providers such as hidden web services. Recently size and content of Tor hidden services are highly concerned because of emerging illegal content. It is necesary to have discovery and analysis approaches to collect and analyze Tor hidden services. Previous in-band1 approaches are mostly based on the protocol and design principles of Tor network, which mostly have a great deal of resource consumption. In this paper, we propose an outof-band approach to discover Tor hidden services, which is simpler and owns less resource consumption: using multiple search engines with specific keywords. In our experiments we collected 173667 *** addresses in all using a single Amazon EC2 instances in 1 hour and found 4857 hidden web services online. We also propose a modified PageRank algorithm: OnionRank Algorithm to evaluate the importance of hidden web services, which concentrates more on Tor users' actions. Results showed that the top-ranking hidden web services are mostly famous in Tor network. © 2016 ACM.

关键词： Web services

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Design and implementation of a C++ memory leak detection tool based on dynamic instrumentation 10

Design and implementation of a C++ memory leak detection too...

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10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2016

作者： Zhou, Liang Fu, Siran Guo, Tao Han, Lifang Cui, Baojiang School of Computer Science Beijing University of Posts and Telecommunications Beijing China China Electric Power Research Institute Beijing China National Engineering Laboratory for Mobile Network Security China

ISBN: (纸本)9781509009848

For C++ programs often appear memory leaks, cross-border access memory and memory does not match the release of error, we have analyzed existing memory error detection tools and methods, implemented in the Linux platform to run the memory testing tool MemDetect prototype based on the open source dynamic instrumentation-Valgrind using function family's memory information block management method and life cycle method. The prototype can detect memory leaks, cross-border access memory and memory does not match the release problems effectively, the validity and efficiency of the prototype are proved by comparing with Purify Rational. © 2016 IEEE.

关键词： Life cycle

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Multiple influence maximization in social networks 10

Multiple influence maximization in social networks

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10th International Conference on Ubiquitous Information Management and Communication, IMCOM 2016

作者： Sun, Honglin Gao, Xiaofeng Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab. of Scalable Computing and Systems Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450341424

Influence Maximization, a technique of social analysis to help marketers select a small group of users to promote their products with the goal of maximizing their influence spread, has been studied for a long time. However, previous researches did not consider the scenario that a company may intend to promote serval kinds of products in the same social network, where each user has different preferences for different categories. Considering this scenario in this paper, we propose the Multiple Influence Maximization (MIM) problem based on the assumption that one seed user can accept serval kinds of goods for free, and at the same time non-seed users have enough purchasing power to accept different promotions from their social friends. To address this issue, we design the corresponding greedy algorithm framework with an approximate radio of 1-1/e if the influence function is submodular. Besides, to simplify the computation of influence spread and considering the properties of real social influence propagation, we propose a novel influence quantitative computation model in which influence function fortunately is submodular. Applying this model into our MIM problem, the experiment results show that the greedy algorithm out-performs serval traditional heuristics in influence spread without loss of performance. © 2015 ACM.

关键词： Method of moments

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