Distributed Datamining and Agent System,security


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Multi Agent based distributed datamining

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Distributed Datamining and Agent System,security

  1. 1. Multi Agent Based De-Centralized Knowledge Discovery and Agent Security: A Review Presentation by: Aman Kumar M.tech -CSE (2nd Sem) Graphic Era University, Dehradun,IndiaMay 26, 2012 1
  2. 2. Agenda Introduction Data Mining Vs. De-Centralized Data Mining Agent, Why Agent? Agent Based De-Centralized Data Mining MADM Systems-An Architectural Approach Advantages of MADM Agents Security Issues Security measures for agent Future Scope Summary ReferencesMay 26, 2012 2
  3. 3. Introduction Data Mining-KDD De-Centralized Environment De-Centralized KDD(DDM) An Agent Multi Agent SystemMay 26, 2012 3
  4. 4. Data Mining General Data Mining Model Data Mining Data Tools WarehouseMay 26, 2012 4
  5. 5. Distributed Data Mining De-Centralized Data Mining model Local Model Aggregation Final Model Local Local Local Model Model Model Data Mining Data Mining Data Mining Algorithm Algorithm AlgorithmMay 26, 2012 5 Site k Site 2 Site 1
  6. 6. Agent , Why Agent? An Software Agent is as user’s personal assistant . Agent can be programmed as compact as possible. Light weight agent can transmitted across the network rather than data that is more bulky. The Designing of DDM Systems Deals With  Great Details of Algorithms used  Reusability  Extensibility  Robustness Hence , the agent characteristics are desirable to use in DDM.May 26, 2012 6
  7. 7. Agent Based DDM  ADDM system concerns three keys characteristics  Interoperability  Dynamic Configuration  Performance Aspects  Application’s of distributed data mining include credit card Authentication, intrusion detection and all this type general and security related applications.  Into this a novel Data Mining Technique inherits the properties of agents.  The DDM applications can be further enhanced with agents.  Better Integration policy with the communication protocols  Provide a view of online parallel processingMay 26, 2012 7
  8. 8. Basic Components of ADDM  An ADDM system can be generalized into a set of components Application Layer Data Mining Layer Agent Grid Infrastructure Layer Fig. Overview of ADDMMay 26, 2012 8
  9. 9. MADM Systems- An Architectural Approach  MADM is the ADDM but equipped with several agents which have particular goal of functionality as:  Resource Agent: Maintaining Meta Data Information  Local Task Agent: Located at the local site  Broker Agent: Working as Advisor agent  Query Agent: KDD System Agent  Pre-Processing Agent: Preparing data for mining  Post Data Agent: Evaluates the performance and accuracy  Result Agent: Aggregate the all local results  Interface Agent: Provide Interface to the real world applications  Mobile Agent: Migrate based on Request and ResponseMay 26, 2012 9
  10. 10. MADM Systems-An ArchitecturalApproach contd…. Data Source On DifferentMay 26, 2012 Sites 10
  11. 11. Agent Security Issues Identification and authentication Authorization and delegation Communication • confidentiality: assurance that communicated information is not accessible to unauthorised parties; • data integrity: assurance that communicated information cannot be manipulated by unauthorised parties without being detected; • availability: assurance that communication reaches its intended recipient in a timely fashion; • non-repudiation: assurance that the originating entity can be held responsible for its communications. Mobility Situated ness Autonomy Agent ExecutionMay 26, 2012 11
  12. 12. Security Measures for agent Protecting agents Trusted hardware Trusted nodes Co-operating agents Execution tracing Encrypted payload Environmental key generation Computing with encrypted functions Un-detachable signaturesMay 26, 2012 12
  13. 13. Security Measures …… Protecting the agent platform Sandboxing and safe code interpretation Proof carrying code Signed code Path histories State appraisalMay 26, 2012 13
  14. 14. Future Scope  Data Mining and web mining is the hot area of research  Integration of KDD and Agent technology can provide a new way to both  For several network security researchers it can provide several new way to find the fraud in the network as provide fast discovery  Real time confidential transaction can be make secure by the integration of Agent TechnologyMay 26, 2012 14
  15. 15. Summary  This presentation presented an overview of :-  Data Mining  Distributed Data Mining  Agent Based DDM  MADM systems as survey based on the information that are exist today.  common components between these systems and gives a description to their strategies and architecture.  Security Measures For Agents  This presentation shows the integrated architectural model of distributed data mining and the agent technology, which provide a optimized performance to the knowledge discovery when the data is not resides in a central site or scattered over the network.May 26, 2012 15
  16. 16. References  [1] IJRIC ISSN: 2076-3328 www.ijric.org E-ISSN: 2076-3336 “. Agent based distributed data mining: AN OVER VIEW “VUDA SREENIVASA RAO, 2009-2010  [2] M. Klusch, S. Lodi, G. Moro. Agent-based Distributed Data Mining: The KDEC Scheme. Intelligent Information Agents - The AgentLink Perspective. Lecture Notes in Computer Science 2586 Springer 2003.  [3] “Distributed Data Mining and Agents” Josenildo C. da Silva, Chris Giannella, Ruchita Bhargava, Hillol Kargupta1;, and Matthias Klusch  [4] Y. Xing, M.G. Madden, J. Duggan, G. Lyons. A Multi-Agent System for Context-based Distributed Data Mining. Technical Report Number NUIG-IT-170503, Department of Information Technology, NUI, Galway, 2003.  [5] “Agent-Based Data-Mining” Winton Davies 15 August 1994  [6] Priyanka Makkar et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 04, 2010, 1237-1244 DISTRIBUTED DATA MINING AND MINING MULTI-AGENT DATA ,Vuda Sreenivasa Rao, Dr. S Vidyavathi  [7] V. Gorodetsky and I. Kotenko. “The Multiagent Systems for Computer Network Security Assurance: frameworks and case studies.” In IEEE International Conference on Artificial Intelligence Systems, 2002, pages 297–302, 2002.  [8] International Journal of Computer Applications (0975 – 8887) Volume 4– No.12, August 2010  23 “A Comparative study of Multi Agent Based and High- Performance Privacy Preserving Data Mining”, Md Faizan Farooqui, Md Muqeem, Dr. Md Rizwan Beg  [9] Future Generation Computer Systems 23 (2007) 61–68 ,www.elsevier.com/locate/fgcs “Distributed data mining on Agent Grid: Issues, platform and development toolkit” Jiewen Luoa,b,_, Maoguang Wangc, Jun Hud, Zhongzhi Shia  [10] Sung W. Baik, Jerzy W. Bala, and Ju S. Cho. Agent based distributed data mining. Lecture Notes in Computer Science, 3320:42–45, 2004.  [11]Xining Li and Jingbo Ni. Deploying mobile agents in distributed data mining. Lecture Notes in Computer Science 4819:322–331, 2007 .May 26, 2012 16 [12]“Mobile agent security” Niklas Borselius Mobile VCE Research Group Information Security Group, Royal Holloway, University of London Egham, Surrey, TW20 0EX, UK ,Niklas.Borselius@rhul.ac.uk
  17. 17. Thank YouMay 26, 2012 17