Lecture Notes in Information Technology

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Lecture Notes in Information Technology  Volume 10        Lecture Notes in Information Technology    2012 International Conference on Affective Computing and  Intelligent Interaction (ICACII 2012)    Taipei, Taiwan, February 27‐28, 2012      Edited by  JiaLuoluo      INFORMATION ENGINEERING RESEARCH INSTITUTE,  USA    iii   Copyright © 2012 Information Engineering Research Institute, USA    All  rights  reserved.  Personal  use  of  this  material  is  permitted.  However,  permission  to  reprint/republish  this  material  for  advertising  or  promotional  purposes  or  for  creating  new  collective  works  for  resale  or  redistribution  to  servers  or  lists,  or  to  reuse  any  copyrighted  component  of  this  work  in  other  works  must  be  obtained  from  the  Information  Engineering  Research Institute.        Information Engineering Research Institute  100 Continental Dr, Newark, DELAWARE 19713, Unite State, USA  http://www.ier‐institute.org          ISBN: 978‐1‐61275‐004‐0  Lecture Notes in Information Technology Vol.10  ISSN: 2070‐1918                              Distributed worldwide by  Information Engineering Research Institute  100 Continental Dr, Newark, DELAWARE 19713, Unite State, USA    E‐mail: adminier‐institute.org                          iv Message from the ICACII 2012 Chair    2012 International Conference on Affective Computing and Intelligent Interaction (ICACII 2012) will  be held in Taipei, Taiwan, February 27‐28, 2012.  Affective computing is the study and development of systems and devices that can recognize,  interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer  sciences, psychology, and cognitive science. While the origins of the field may be traced as far back  as to early philosophical enquiries into emotion, the more modern branch of computer science  originated with Rosalind Picard's 1995 paper on affective computing. A motivation for the research  is the ability to simulate empathy. The machine should interpret the emotional state of humans and  adapt its behavior to them, giving an appropriate response for those emotions.  Intelligent Interaction research theme seeks to enhance human‐machine interface design through  the  optimization  of  state‐of‐the‐art  technology  development  and  engineering  of  multimodal  interface design concepts. II also seeks to explicate the mechanisms of human perception, cognition,  and action that are relevant to industrial, military, and consumer products. Projects in HCII involve  the close collaboration of computer scientists, electrical engineers, neuroscientists, linguists and  others in pursuit of knowledge relevant to the design of interfaces for human‐computer systems.  ICACII 2012 will be the most comprehensive conference focused on the various aspects of advances  in Affective Computing and Intelligent Interaction. The conference is intended to bring together the  researchers  and  scientists  working  in  different  aspects  of  affective  computing  and  intelligent  interaction. In addition to the contributed papers, the conference committee has invited papers by  active researchers from various countries in relevant topic areas. Internationally known experts are  invited to give keynote speeches.  Welcome to ICACII 2012; welcome to Taipei. Taipei is the capital of Taiwan. It is in the northern part  of the island in a basin between the Yangming Mountains and the Central Mountains. It is, with 2.6  million inhabitants, the fourth largest administrative area of Taiwan, after New Taipei, Kaohsiung  and Taichung. However, the Greater Taipei metropolitan area, which encompasses the central Taipei  City along with the surrounding New Taipei City and Keelung, represents the largest urban cluster in  Taiwan  with  nearly  7  million  people.  Taipei  serves  as  the  island's  financial,  cultural,  and  governmental center.   It is hoped that the present book will be useful to scientists, both specialists and graduate students.  I express my deep gratitude to all contributors of this book who worked very hard to make this  project  successful.  If  our  efforts  came  to  the  present  result,  it  was  not  without  ideas,  encouragement and support from Publishing Manager that must be gratefully acknowledged.  JiaLuoluo, Kinmen County Ningxiang University Avenue, Taiwan  v ICACII 2012 Organizing Committee    Honorary Chair and Keynote Speakers Chin Chen Chang  Feng Chia University, Taiwan  Wei Lee  Melbourne ACM Chapter Chair, Australia  General Chairs  Zhenghong Wu  East China Normal University, China  Minli Dai  Suzhou University, China  Publication Chair  JiaLuoluo  Kinmen County Ningxiang University Avenue, Taiwan  Organizing Chairs  Jia‐chin Chang  National Chung Hsing University, Taiwan  Kaikai Yang  National Chiayi University, Taiwan  Program Chair  DehuaiYang  Huazhong Normal University, China  Program Committee  Zhou William  Belize University, Beliza  Anne Wing  Yangon University, Myanmar  Khing Mg Win Fay  University of Guyana, Guyana  Qihai Zhou  Southwestern University of Finance and Economics, China  Zhenghong Wu  East China Normal University, China  Tatsuya Akutsu  ACM NUS Singapore Chapter, Singapore  AijunAn  National University of Singapore, Singapore  Yuanzhi Wang  Anqing Teachers' University, China  YiyiZhouzhou  Azerbaijan State Oil Academy, Azerbaijan  vii Table of Contents Volume 10 Detecting and Resolving Constraint Conflicts in Role-Based Access Control QIU Jiong, MA Chen-hua ······························································································································ 1 The Research and Simulation of Multilayered Satellite Routing Algorithms Liu Tong, Yang Chunxiu, Zhang Linbo ·········································································································· 8 A Multiple Attribute Decision Making-Based Access Selection for Heterogeneous WCDMA and WLAN Networks Fan Ning, Pinjing Zhang ······························································································································ 15 A Review of Ensemble Method Hui-lan LUO, Zhong-Ping LIU ···················································································································· 22 Research on Innovative Practice of Informatization in China Rural Areas Ruyuan Li, Zhi’an Wang, Weihua Zheng, Junxia Yan ··················································································· 27 Dynamic Test for Elastic Modulus and Damp Ratio of Three Layers Plywood Wang Zheng, Yang Xiaojun, Wang Xiwei, Li Jie, Fan Wubo ········································································· 32 Automated Accompaniment System Based on Bayesian Mining of Score Context Ryosuke Yamanishi, Ryogo Okamur, Shohei Kato ························································································ 38 The Decision Model of Customer Segmentation Censoring Hui-Hsin Huang ··········································································································································· 44 EEG-Based Brain Computer Interface for Game Control Xing Song, S. Q. Xie, K. C. Aw ····················································································································· 47 Application of PARSEC Geometry Representation to General Airfoil for Aerodynamic Optimization R. Mukesh, K. Lingadurai, A. Muruganandham ··························································································· 55 Application of Ant Colony Algorithm in Emotion Clustering of EEG Signal Liu Hongxia, Wu Guowen, Luo Xin ·············································································································· 61 The Storage and Management of Distributed Massive 3D Models based on G/S mode Miao Fang, Xie Yan, Yang Wenhui, Chai Sen ······························································································· 66 The Current Situation of Green Tourism in China Diao Zhibo ···················································································································································· 72 Improvement of the Mutual Authentication Protocol for RFID Juseok Shin, Sejin Oh, Cheolho Jeong, Kyungho Chung, Yonghwan Kim, Sanghoon Kim, Kwangseon Ahn ············································································································································ 76 A Mutual Authentication Protocol in RFID Using CRC and Variable Certification Key Sejin Oh, Juseok Shin, Cheolho Jeong, Jaekang Lee, Sungsoo Kim, Seungwoo Lee, Kwangseon Ahn ··················································································································· 84 A Low Power 32bit Microcontroller and Its Application on Handheld Financial Transaction Terminal Yinchao Lu, Weiwei Shan, Haolin Gu ··········································································································· 90 Digital Video Watermarking Algorithm Based on Blocked Wavelet Transform Sun Cheng, Gao Fei, Gong Zhaoqian ·········································································································· 95 ix Data Mining Based Crime-Dependent Triage in Digital Forensics Analysis Rosamaria Bertè, Fabio Marturana, Gianluigi Me, Simone Tacconi ························································ 101 Deployment of QoS Bandwidth Guarantee Based on Burst Traffic Detection Technology Wang Sunan, Zhang Jianhui, Zhao xin, Zhang Xiaohui ············································································· 107 An Inverted Index Method for Mass Spectra K-Nearest Neighbor Queries Houjun Tang, Xi Liu, Honglong Xu, Kezhong Lu, Gang Liu, Yuhong Feng, Hong Zhou, Rui Mao ·································································································································· 115 Fuzzy Correlation with the Issues of Study in Domestic Campus or Intent to Study Abroad Dian-Fu Chang, Wen-Ching Chou ············································································································· 123 Using Soft Computing to Assess the Issue of Time Management Dian-Fu Chang, Wen-Ching Chou ············································································································· 129 Predicting Relapse of Hepatocyte Cancer by Combing Regression and Classification Using SVM Kazuhiro Nakada, Hayato Ohwada, Hiroyuki Nishiyama ·········································································· 135 A New Updating Strategy in Simulating Emergency Evacuation Yugang Zhang, Hongjun Xue ······················································································································ 141 A New Literature Search System with Thesaurus for Biomedical Literatures Kazuhiro Tanaka, Hayato Ohwada ············································································································· 146 Alternative Methodology of Complex Social System: Determining the Level of Agency and Its Relations Bogart Yail Márquez, José Sergio Magdaleno-Palencia, Miguel López, Arnulfo Alanis Garza ················ 152 Estimate the Intrinsic Dimension of a Metric Space Using the Eigenvalues of the Pair-wise Distance Matrix Xi Liu, Houjun Tang, Zhao Jiang, Pang Yue, Ye Cai, Haijun Lei, Hong Zhou, Rui Mao ··························· 159 Improved Usability of Object Structure and Error Location Analysis Keiji Takiguchi, Hayato Ohwada ················································································································ 165 Task Merger and Spanning Tree Based Grid Tasks Rescheduling Tingwei Chen, Jingsen Wang, Shanjie Zhou ······························································································· 171 Path Planning of a Data Mule for Data Collection in the Sensor Network by Using an Improved Clustering-Based Genetic Algorithm Ko-Ming Chiu, Jing-Sin Liu ······················································································································· 177 Rule Extraction from SOM for Academic Evaluation Sathya Ramadass, Annamma Abhraham ···································································································· 184 EEG Analysis of Drivers under Emergency Situations Luzheng Bi, Zhi Wang, Xin-an Fan ············································································································· 190 Emotion Image Retrieval Based on SOFM Yang Tan, Guowen Wu, Xin Luo ················································································································· 194 Image Retrieval by Optimal Distance Measure based on Metric Matrix Learning Algorithm Xin Luo, Yang Tan, Guowen Wu ·················································································································· 199 Integrated Test Framework Model for E-business Systems Pasha Vejdan Tamar, Abbas Asosheh, Hourieh Khodkari ·········································································· 205 Design and Enhancement of Mandarin Emotional Speech Database Liqin Fu, Hongli Jin, Xinjie Wu ·················································································································· 212 Knowledge Management Platform Based on the Environmental Monitoring System with Energy Harvesting Sensor Motes for Tea Farming Eiji Aoki, Ken Kudo, Akira Fukuda, Tsuneo Nakanishi, Shigeaki Tagashira, Takashi Okayasu, Naoyuki Tsuruda, Satoru Yamasaki, Yasuhito Imura ·················································································· 217 x Mentally Framing a Three-dimensional Object from Plane Figures Increases Theta-Band EEG Activity Koji Kashihara ··········································································································································· 224 Semantic Categorization of Emotional Pictures Koji Kashihara ··········································································································································· 229 Embodied Conversational Agent Model Hima Bindu Maringanti, Aditya goil, Indraneel Srivastav, Sonali Satsangi ·············································· 235 The Novel Application of Bioelectrical Impedance Analysis with Back Propagation Artificial Neural Network to Assess the Body Compositions of Lower Limbs in Elite Male Wrestler Tsong-Rong Jang, Hsueh-Kuan Lu, Ruey-Tyng Kuo, Yu-Yawn Chen, Kuen-Chang Hsieh ························· 241 The Establishment of Bioelectrical Impedance Analysis System with Neural Network Model to Estimate Segmental Body Compositions in Collegiate Wrestlers Tsong-Rong Jang, Yu-Yawn Chen, Hsueh-Kuan Lu, Cai-Zhen Mai, Kuen-Chang Hsieh ··························· 250 Automated Text Illustrator Based on Keyword Sense Tagging Savindhi Samaraweera, Ravindra Koggalage ···························································································· 259 Using ASM-optical Flow Method and HMM in Facial Expression Recognition Wencang Zhao, Junbo Zhang ····················································································································· 265 Facial Illumination Compensation Based on the Wavelet Transform Wencang Zhao, Chengcheng Zhao ············································································································· 270 Robust Cognitive System Engineering Based on Control Frame of Cognition Rui Wang, Keiji Watanabe ·························································································································· 275 CT Liver Segmentation Based on Fuzzy Cellular Neural Networks and Its Stability P. Balasubramaniam, M. Kalpana ·············································································································· 281 GQ2 vs. ECC: A Comparative Study of Two Efficient Authentication Technologies Louis C. Guillou, Marc Joye ······················································································································· 289 Design and Implementation of an Adaptive Control Mechanism for Standby Power Detection and Saving Shun-Chieh Lin, Huan-Wen Tsai, Yi-Lin Chiang, Tsung-Lin Tsai ······························································· 295 Solution of Matrix Riccati Differential Equation of Optimal Fuzzy Controller Design for Nonlinear Singular System with Cross Term Using SIMULINK M. Z. M. Kamali, N. Kumaresan, Kuru Ratnavelu ····················································································· 304 Conflict Detection in Autonomic Systems Using Petri Networks Siddhartha Moraes Amaral de Freitas, Catalin Meirosu, Djamel Fawzi Hadj Sadok ······························· 310 Personalized Predictive Model for Mobile Value Added Services Meera Narvekar, S.S Mantha ······················································································································ 316 A Dynamic Workflow Model Based on Petri Net and Instance Migration Huifang Li, Ming Zhang ····························································································································· 320 Safety Distance by Simulation and Collision Avoidance on a Road’s Danger Zones SCHREIBER Peter, MORAVCIK Oliver, TANUSKA Pavol, VAZAN Pavol, VRABEL Robert, BARTUNEK Marian, HUSAR Peter ··············································································· 326 Solving a Four-Point Boundary Value Problem for Dynamical Systems with High-Speed Feedback with MATLAB Robert Vrabel, Peter Schreiber, Oliver Moravcik, Ingrida Mankova ························································· 332 A New Representation of Emotion in Affective Computing Leonid Ivonin, Huang-Ming Chang, Wei Chen, Matthias Rauterberg ······················································· 337 xi Improved Huffman Algorithm in Multi-channel Synchronous Data Acquisition and Compression System Ma Xian-Min,Zhou Gui-Yu ························································································································· 344 Affective Smart City: A first Step for Automatic Governance Francesco Rago, Stefano G. Rago, Alberto Panico ···················································································· 351 Solving the Airlines Recovery Problem Considering Aircraft Rerouting and Passengers Meilong Le, Chenxu Zhan, Congcong Wu ·································································································· 358 Performance of Improved Short-Length Raptor Coded Frequency- Hop Communication in Partial-Band Interference ZENG Xianfeng, GAO Fei, BU Xiangyuan ································································································ 365 Analysis of Highway Rear-end Accidents Based on FTA Method Yun Jiang ···················································································································································· 370 Reputation Management of Art Communication in Internet Li Cui, Juan Han ········································································································································ 376 Study on Content Clustering in E-Journal Operation Juan Han, Li Cui ········································································································································ 379 A Flexible Workflow Management System Architecture Based on SOA Huifang Li, Cong Chen ······························································································································· 382 Knowledge Based Interactive Smart Camera Rustam Rakhimov Igorevich, Pusik Park, Jongchan Choi, Dugki Min ······················································ 388 Performance Evaluation of Modern Intel x86 Processors through Computer Capacity Boris Ryabko, Andrey Fionov ····················································································································· 394 Positional Conformity Degree Checking Method of Spatial Data Quality Dou Shiqing, Du Jiliang, Yu Fujun ············································································································· 400 Rapid Features Detection Using Improving Algorithm for Self-Localization in a DSP Board Xing Xiong, Byung-Jae Choi ······················································································································ 409 The Development of Corridor Identification Algorithm Using Omni-directional Vision Sensor ARTHAYA Bagus, WU Mellisa···················································································································· 412 Coordination of Ambulance Team Agents in Rescue Simulation Using Auction Strategy Pooya Deldar Gohardani, Peyman Ardestani, Behrooz Masoumi, Mohammad Reza Meybodi, Siavash Mehrabi ···························································································· 418 Author Index ··················································································································································· 426 xii 2012 International Conference on Affective Computing and Intelligent Interaction Lecture Notes in Information Technology, Vol.10 Detecting and Resolving Constraint Conflicts in Role-Based Access Control 1,a 2,b QIU Jiong , MA Chen-hua 1 Department of Computer Science & Technology, Hangzhou Dianzi University, Hangzhou 310018, China 2 Engineering & Computer Graphics Institute, Zhejiang University, Hangzhou 310027, China a,b mchmazju.edu.cn Keywords: Role-based Access Control; Constraint Conflict; Conflict detection and Resolution Abstract. The detection and resolution of constraint conflicts in RBAC have been overlooked and remain a significant research challenge. To address these concerns, in this paper, we classify constraint conflicts into two categories: internal constraint conflicts that occur when two or more constraints are deemed incompatible with each other and external constraint conflicts that occur when the configuration of a RBAC system violates the defined constraints, and propose a set of detection rules for these conflicts. Furthermore, we introduce the notions of resolution value and valid resolution value, and show how they are useful in guiding external constraint conflict resolution. Introduction Role-based access control (RBAC) is known as the most suitable access control model for enterprise organizations. The importance of the constraints in RBAC has been recognized for a long time1. In the past decade, a considerable amount of work 2-5 has been done on RBAC constraints. However, the focus of these researches has been predominantly on the specification of RBAC constraints. Effective conflict detection and resolution methods used to maintain the consistency between constraints have been overlooked and remain a significant research challenge. Strembeck 6 discussed the conflict checking of separation of duty constraints in RBAC and presented conflict checking methods as implemented in the xoRBAC software component. Moon 7 addressed the issue of conflict detection to maintain the consistency of permission assignment constraints in RBAC. However, the conflict detection and resolution methods used to maintain the consistency between constraints have not been addressed. Janpitak 8 proposes a simple but effective model to solve the problem of the dynamic separation of duties.The conflict of interest can be verified at run time. But the model cannot support role hierarchies in RBAC. To address the problem, we propose in this paper the following approaches: z We classify constraint conflicts into two categories: internal constraint conflicts and external constraint conflicts. Internal constraint conflicts refer to the conflicts exhibited by two or more incompatible constraints. External constraint conflicts refer to the conflicts exhibited by the configuration of a RBAC system and the constraints defined in the system. z We give several conflict detection rules for internal constraint conflicts by the definition of Foundation Item: Project (No. 2009C03015-1) supported by the Large Science and Technology Special Program of Zhejiang Province. Corresponding author: QIU Jiong, Associate Professor; Tel: +86-13805742886; E-mail: mchmazju.edu.cn 978-1-61275-004-0/10/25.00 ©2012 IERI ICACII2012 1constraint conflict graph. An effective detection rule for external constraint conflicts is proposed by defining two concepts, RBAC configuration graph and conflict pattern. An approach to external constraint conflict resolution is proposed. RBAC model We will base our discussion on RBAC96. In this section, we provide an overview of the central concepts within the model. A role hierarchy is a partial order on roles called the inheritance relation, written as ≥, where r ≥ r only if all permissions of r are also permissions of r . Users are associated i j j i with roles using the user–role assignment relation UA⊆U × R. If there exists a pair (u, r)⊆UA, then role r is explicitly assigned to user u. Permissions are associated with roles using the permission-role assignment relation PA⊆P × R. If there exists a pair (p, r)⊆PA, then permission p is explicitly assigned to role r. Constraints are a powerful mechanism for laying out higher-level organizational policy. The specification of constraints in RBAC Constraints proposed in RBAC models can be classified into three broad categories: 1. Separation of Duty (SoD) constraints: SoD constraints aim at reducing the risk of fraud by not allowing any individual to have sufficient authority within the system to perpetrate a fraud on his/her own. In this paper, we focus on static SoD constraints. Three varieties of static SoD constraints have been proposed so far: z Conflicting role constraints: Let CR represent the collection of conflicting role sets, CR=cr ,cr ,…,cr ,where cr (i=1,…,n) denotes a conflicting role set. Two or more roles of a 1 2 n i conflicting role set cannot be assigned to the same user. z Conflicting permission constraints: Let CP represent the collection of conflicting permission sets, CP=cp ,cp ,…,cp ,where cp (i=1,…,m) denotes a conflicting permission set. Two or 1 2 m i more permissions belonging to a conflicting permission set cannot be assigned to the same role. z Conflicting user constraints: Let CU represent the collection of conflicting user sets, CU=cu ,cu ,…,cu ,where cu (i=1,…,t) denotes a conflicting user set. Two conflicting users 1 2 t i cannot have roles in the same conflicting role set. 2. Cardinality constraints: A cardinality constraint can be formally defined as (r, n). Where r is the role associated with the constraint; n denotes the numerical limitation for the role. 3. Prerequisite constraints: Prerequisite constraints are defined based on competency and appropriateness whereby a user can be assigned role r only if the user is already a member of role 1 r , or a permission p can be assigned to a role only if the role already possesses permission p . 2 1 2 Constraint conflict detection and resolution Definition 1 (Internal Constraint Conflict). Internal constraint conflicts occur when two or more constraints are deemed incompatible with each other. For example, there exists a prerequisite role constraint in which role r is defined as a prerequisite role of role r ; whereas, there exists another 1 2 prerequisite role constraint in which role r is defined as a prerequisite role of role r . In this case, 2 1 the two constraints are contradictory and exhibit an internal constraint conflict. Definition 2 (External Constraint Conflict). External constraint conflicts occur when the configuration of a RBAC system doesn’t satisfy the constraints defined in the system. For example, if a new conflicting role set consisting of two roles r and r is created, and there is an existing 1 2 inheritance relationship between r and r in the role hierarchy, then an external constraint conflict 1 2 occurs. Internal constraint conflict detection. Definition 3 (Constraint Conflict Graph). Constraint conflict graph is used by the security administrator to understand internal constraint conflicts easily and to detect internal constraint conflicts effectively. Constraint conflict graph is a multi graph, 2denoted as G(CC)=(V, E). Where V is the set of vertices in G(CC), composed of the users, roles and permissions associated with constraints. An edge in E can be denoted as e:(v , v , label, id), 1 2 where v and v represent the source vertex and the target vertex of e respectively; label denotes a 1 2 possible constraint relation between v and v ; id is the identifier of the constraint corresponding to 1 2 the edge. For an edge e, it may be one of the following cases: z e:(v , v , conflicting, id), where v and v are two members of a conflicting element set (e.g., a 1 2 1 2 conflicting role set, a conflicting permission set, and a conflicting user set), and id is the identifier of the SoD constraint associated with the conflicting element set. z e:(v , v , prerequisite, id), where v is the target element of a prerequisite constraint (e.g., the 1 2 1 target role of a prerequisite role constraint, and the target permission of a prerequisite permission constraint), v is a member of the prerequisite element set of the constraint (e.g., the 2 prerequisite role set of a prerequisite role constraint), and id is the identifier of the prerequisite constraint. For instance, there exist five constraints as defined below. cr : (r , r , r ); prc : (r , r ); ppc : (p , p ); ppc : (p , p ); ppc : (p , p ) 1 1 2 3 1 1 2 1 1 2 2 2 3 3 3 1 Where cr is a conflicting role set in which r , r and r are defined as conflicting; prc is a 1 1 2 3 1 prerequisite role constraint in which r is defined as the prerequisite role of r ; ppc , ppc and ppc 2 1 1 2 3 are three prerequisite permission constraints that define p , p and p as the prerequisite permissions 2 3 1 of p , p and p respectively. The constraint conflict graph constructed for these example constraints 1 2 3 is as shown in Figure 2. Internal constraint conflicts can be detected by the construction of G(CC). Fig. 1.. Example constraint conflict graph Rule 1. If there exist two edges between two vertices v to v in G(CC), one of the edge is 1 2 labeled as conflicting and the other is labeled as prerequisite, then the two constraints corresponding to the two edges are inconsistent and internal constraint conflicts occur. Rule 2. If there is a loop in G(CC), consisting of a sequence of edges labeled as prerequisite, then the prerequisite constraints corresponding to these edges are inconsistent and internal constraint conflicts occur. Figure 1 shows an example of such conflicting situation. There exists a loop composed of three edges labeled as prerequisite, and the three constraints ppc , ppc and ppc are 1 2 3 inconsistent. Rule 3. For two cardinality constraints (r , n ) and (r , n ), if (r = r ) ∧ (n≠n ), then they are 1 1 2 2 1 2 1 2 inconsistent and exhibit an internal constraint conflict. External Constraint Conflict Detection. External constraint conflicts arise when the configuration of a RBAC system doesn’t satisfy the constraints defined in the system. For example, if a new conflicting permission constraint is created in which two permissions p and p are defined 1 2 as conflicting, and there exists a role r that has been assigned both p and p in the current RBAC 1 2 configuration, then in this case, the current configuration violates the new constraint and external constraint conflicts occur. Definition 6 (RBAC Configuration Graph). RBAC configuration graph is a directed one, denoted as G(RCG)=(V, E), where V is the set of vertices in G(RCG), composed of all users, roles and permissions in the RBAC system, i.e., N=U∪R∪P. E is the set of edges in G(RCG) defined 3by existing explicit assignment relations in the system. If vertex v is explicitly assigned to vertex v 1 2 (e.g., a role is explicitly assigned to a user), then an edge e starting from v to v is created, denoted 1 2 as e:(v , v ), where v and v represent the source vertex and the target vertex of e respectively. 1 2 1 2 For two vertices v and v in the graph, if there exists a path starting from v to v , then there is i j i j an assignment relation between v and v , denoted as assign(v , v ). An assignment relation between i j i j two vertices may be an explicit assignment relation or an implicit assignment relation, which is a chain of explicit assignment relations. For two vertices v and v in the graph, if there exist no path i j starting from v to v , then there is no assignment relation between v and v , denoted as ¬ assign(v , i j i j i v ). An example RBAC configuration graph is as shown in Figure 2. j Fig. 2.. Example RBAC configuration graph There are six roles (r , r , r , r , r and r ), and five users (u , u , u , u and u ) in the graph. For 1 2 3 4 5 6 1 2 3 4 5 example, role r is explicitly assigned to role r . Note that there are six implicit assignment relations 6 4 assign(r , u ), assign(r , u ), assign(r , u ), assign(r , u ), assign(r , u ) and assign(r , u ) in the 4 1 4 2 4 3 6 1 6 2 6 3 graph, and each of them is a chain of explicit assignment relations. For instance, assign(r , u ) is a 6 1 chain of three explicit assignment relations, assign(r , u ), assign(r , r ) and assign(r , r ), i.e., r is 1 1 4 1 6 4 6 implicitly assigned to u in the presence of the role hierarchy. 1 Definition 7 (Conflict Pattern). For a given constraint c, if the combination of certain assignment relations assign , …, assign results in the violation of the constraint, it can be said that 1 n (assign , …, assign ) is the conflict pattern of c, denoted as: Conflict_Pattern (c): (assign , …, 1 n 1 assign ). For instance, for a conflicting role constraint cr:(r , r , …, r ), we can identify the conflict n 1 2 n pattern for the constraint as described below. Conflict_Pattern (cr): assign (r , u), assign (r , u), r , r∈ cr, i≠j i j i j The conflict pattern states that the assignments of both two conflicting roles r and r to the same i j user u can lead to the violation of the constraint. According to the pattern, if there exist two assignment relations in the RBAC configuration graph that match the specified relations defined in the pattern, then an external constraint conflict occurs. The conflict patterns for different types of constraints proposed in RBAC are as shown in Table 1. Table 1. Conflict patterns for constraints proposed in RBAC Constraint Conflict pattern description cr:(r , r , …, Conflict_Pattern(cr):assign The conflict pattern states that the assignments of both two conflicting 1 2 r ), cr is a (r , u), assign (r , u), r , r∈ cr, roles r and r to the same user u may lead to the violation of the n i j i j i j conflicting role constraint. i≠j constraint cp:(p , p , …, Conflict_Pattern(cp):assign The conflict pattern states that the assignments of both two conflicting 1 2 p ), cp is a permissions p and p to the same role r may lead to the violation of the (p , r), assign (p , r), p , p∈ n i j i j i j conflicting cp, i≠j constraint. permission constraint 4cu:(u , u , …, Conflict_Pattern(cu):assign The conflict pattern states that the assignments of both two conflicting 1 2 u ), cu is a users u and u to roles in the same conflicting role set may lead to the n (r, u ), assign (r, u ), u , u∈ cu, i j i i i j conflicting user violation of the constraint. i≠j ,r∈cr constraint c:(r, n), c is a Conflict_Pattern(c):assign( The conflict pattern states that if the number of users owned by role r cardinality r, u ), assign (r , u ),…, exceeds the maximum number n, then the cardinality constraint is 1 2 constraint assign(r, u ), t n t violated. c: (tr, Conflict_Pattern(c):assign(t The conflict pattern states that if role tr is assigned to user u without PreRoleSet), c is all roles in PreRoleSet have been assigned to u, then the prerequisite role r, u), ¬ assign (r , u), r∈ i i a prerequisite PreRoleSet constraint is violated. role constraint c: (tp, Conflict_Pattern(c):assign(t The conflict pattern states that if permission tp is assigned to role r PrePermSet), c is p, r), ¬ assign (p , r), p∈ without all permissions in PrePermSet have been assigned to r, then the i i a prerequisite prerequisite permission constraint is violated. PrePermSet permission constraint Rule 4 (External Constraint Conflict Detection). In G(RCG), if there exist assignment relations that match the specified relations defined in a conflict pattern, then external constraint conflicts occur. Each external constraint conflict can be denoted as a combination of certain existing assignment relations in G(RCG). For example, if there is a conflicting role constraint in which r , r 1 2 and r in Figure 2 are defined as conflicting, then the analysis of Figure 2 reveals five external 4 constraint conflicts due to the violation of the constraint. Each conflict is a combination of two assignment relations. conflict : assign (r ,u ), assign (r ,u ); conflict : assign (r ,u ), assign (r ,u ) 1 1 1 2 1 2 1 1 4 1 conflict : assign (r ,u ), assign (r ,u ); conflict : assign (r ,u ), assign (r ,u ) 3 2 1 4 1 4 1 2 4 2 conflict : assign (r ,u ), assign (r ,u ) 5 1 3 4 3 External Constraint Conflict Resolution. Since each external constraint conflict is a combination of some assignment relations in G(RCG), the removal of at least one of the assignment relations may lead to the resolution of the conflict. For example, conflict in Figure 2 can be 3 resolved by the removal or either assign (r ,u ) or assign (r ,u ). Since the removal of different 2 1 4 1 edges may have different impact on conflict resolution, we would like to remove the edge that has the greatest impact, such that many conflicts can be resolved at the same time with little effort. For instance, to resolve conflict , we can remove e , e or e . The removal of e results in the 3 1 4 8 1 resolution of three conflicts conflict , conflict and conflict . While the removal of e results in the 1 2 3 8 removal of four conflicts conflict , conflict , conflict , and conflict . 2 3 4 5 Therefore, in order to resolve conflicts effectively, we must compute the number of conflicts covered by each edge involved in conflict situations. Definition 8 (Edge Cover Set). For an external constraint conflict conflict, let i EdgeCoverSet(conflict ) represent the set of edges covered by the conflict, composed of all edges in i G(RCG) that are associated with the assignment relations in the conflict. For example, the edge cover sets of the five conflicts shown above are listed as follows: EdgeCoverSet(conflict ): (e ,e ); EdgeCoverSet(conflict ): (e ,e ); 1 1 4 2 1 8 EdgeCoverSet(conflict ): (e ,e ,e ); EdgeCoverSet(conflict ): (e ,e ); 3 1 4 8 4 2 8 EdgeCoverSet(conflict ): (e ,e ) 5 3 8 Definition 9 (Resolution Value). For an edge e in edge cover sets, we can identify the number of conflicts resolved by the removal of the edge, which is defined as the resolution value of the edge, denoted as ResolveValue(e). ResolveValue(e) = ∑EdgeCoverSet(conflict ), e∈ EdgeCoverSet(conflict), that is, i i ResolveValue(e) is the number of edge cover sets of which edge e is a member. For example, the resolution value of each edge in the above edge cover sets is calculated and shown as below. ResolveValue(e ) = 3; ResolveValue(e ) = 1; ResolveValue(e ) = 1; 1 2 3 ResolveValue(e ) = 2; ResolveValue(e ) = 4 4 8 Another important issue to note is that the removal of an edge involved in conflicting situations may lead to new conflicts. Let us further assume that there is an existing prerequisite role constraint 5in which role r is defined as a prerequisite role of r in Figure 2. In this case, although the removal 1 5 of edge e can resolve three existing conflicts, conflict , conflict and conflict , it may result in the 1 1 2 3 violation of the prerequisite role constraint and a new conflict conflict : assign (r ,u ) , ¬ assign 6 5 1 (r ,u ) may occur. Given this informal analysis, we introduce a new concept of valid resolution 1 1 value to identify the real impact of the removal of an edge on conflict resolution. Definition 10 (Valid Resolution Value). For an edge e in edge cover sets, the valid resolution value of the edge is its resolution value less the number of new conflicts caused by the removal of the edge. For example, the valid resolution value of e is 2. Given the analysis above, we define the 1 following approach to external constraint conflict resolution. Step1. Identify the edge cover set for each detected external constraint conflict. Step2. Calculate the resolution value and valid resolution value for each edge in edge cover sets. Step3. Identify the set of edges in which each edge’s resolution value is equal to its valid resolution value. If the set is not empty, identify and select an edge with the maximal resolution value and remove the edge; if the set is empty, identify and select an edge with the maximal valid resolution value and remove the edge. If the removed edge is associated with an immediate inheritance relationship, then new immediate inheritance relations need to be added between the immediate descendants of its source vertex and target vertex in the role hierarchy, and corresponding edges should be created in G(RCG). Step4. Calculate the resolution value and valid resolution value for each edge involved in the remaining conflicts. Step5. Repeat step 3 and step 4 until no conflict remains. Conclusions and future work In this paper, we provide approaches to help security administrators in RBAC systems to construct a consistent constraint schema. Constraint conflicts are classified into two categories: internal constraint conflicts and external constraint conflicts. A set of internal constraint conflict detection rules is defined to guarantee the exemption of inconsistency and ambiguities within constraints. When a new constraint is created, the current configuration of a RBAC system may be in conflict with the constraint and external constraint conflicts occur. By the identification of conflict patterns and the construction of RBAC configuration graph, we can detect external constraint conflicts effectively. To guide the resolution of external constraint conflicts, we present new concepts of resolution value and valid resolution value that are useful to guide the resolution process since they represent the effect that the removal of an explicit assignment will have on the resolution of conflicts. References 1 JAEGER T. On the increasing importance of constraintsC// Proceedings of 4th ACM Workshop on Role-Based Access Control, Fairfax, Virginia: ACM, 1999: 33–42. 2 AHN G J. The RCL 2000 language for specifying role-based authorization constraintsD. George Mason University, 2000. 3 LI N H, TRIPUNITARA M V, BRIZI Z. On mutually exclusive roles and separation of dutyC// Proceedings of the 11th ACM Conference on Computer and Communications Security, New York: ACM, 2004: 42-51. 4 SOHR K, AHN G J, GOGOLLA M. Specification and validation of authorization constraints using UML and OCLC// Proceedings of 10th European Symposium on Research in Computer Security, Milan, Italy: Springer Verlag, 2005: 64-79. 65 HELIL N, RAHMAN K. RBAC constraints specification and enforcement in extended XACML C// Proceedings of 2010 International Conference on Multimedia Information Networking and Security (MINES), Nanjing, China: IEEE, 2010: 546-550. 6 STREMBECK M. Conflict checking of separation of duty constraints in RBAC implementation experiencesC// Proceedings of the Conference on Software Engineering, Innsbruck, Austria: ACTA, 2004: 1-6. 7 MOON C J, PAIK W J, KIM Y G, KWON J H. The conflict detection between permission assignment constraints in role-based access controlC// The 1st SKLOIS Conference on Information Security and Cryptology, Beijing, China: Springer Verlag, 2005: 265-278. 8 JANPITAK N, SATHITWIRIYAWONG C. Run-time enforcement model for dynamic separation of duty C// Proceedings of 2010 International Symposium on Communications and Information Technologies (ISCIT), Tokyo, Japan: IEEE, 2010: 115-120. 72012 Interna tional Conference on Affective Computing and Intelligent Interaction Lecture Notes in Information Technology, Vol.10 The Research and Simulation of Multilayered Satellite Routing Algorithms a b c Liu Tong , Yang Chunxiu , Zhang Linbo Harbin Engineering University, China, Harbin a b c liutonghrbeu.edu.cn, ycx05081126.com, zhanglinbohrbeu.edu.cn Keywords: satellite network; routing algorithm; MLSR; delay report Abstract. Based on the deep discussion and the research of existing satellite routing algorithms, this paper puts forward a suitable routing algorithm (NMLSR) for LEO&MEO&GEO multilayer satellite network. This algorithm gives full play to the ground gateway in communication based on MLSR. Using the regularity and predictability of the satellite communication network, let the ground gateway store and transmit part of link information. This method can reduce routing computation cost and shorten the router update time. At the same time, to enhance capacity of the the network to adapt to the sudden traffic, NMLSR increases accesses of monitoring the satellite network traffic flow and chosing satellite. Introduction As an important part of the third generation mobile communication, the satellite communication has become a powerful means of modern communication, because of its prominent advantages, such as the global coverage, simply accessing, extensible, and variable bandwidth according to the needs. Compared with single-layer LEO satellite network, the multilayer satellite network has advantages of high utility of space spectrum, network flexibility, strong survivability, and diversified functions etc 1-3. Satellite communication also gradually changes from the previous work mode of the single heavenly body, pure forwarding to the one that consist of many stars and has the ability to deal with 4,5 things . Facing the complex satellite communication network, the satellite routing technology as the core of the satellite network technology, determines the performance of the whole network system. In recent years, people began to research the multilayer satellite network routing algorithm and multi-layer satellite routing algorithm MLSR put forward by Akyildiz, is one of the most representative of satellite routing algorithms. The simulation results show that the performance of the MLSR algorithm is the same as the shortest path routing except for a short oscillatory phase when the 4 hops are switched to a higher satellite layer . But, MLSR algorithm still has several problems following: (1) Dose not make full use of the regularity and forecasting of the satellite network. Satellites have to send heavy ping messages to obtain the link delay in the network, which leads to high routing computation cost and increases the system burden. (2) Negative the effect of ground gateways in the routing computation. All the routing computation and management tasks is assigned to GEO and MEO satellites, which reduces the life of the whole system and anti-destroying ability. Supported by the Fundamental Research Funds for the Central Universities: HEUCF110802. 978-1-61275-004-0/10/25.00 ©2012 IERI ICACII2012 8

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