Multi Agent based distributed datamining

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,India
May 26, 2012 1

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
 References
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3. Introduction
 Data Mining-KDD
 De-Centralized Environment
 De-Centralized KDD(DDM)
 An Agent
 Multi Agent System
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4. Data Mining
General Data Mining Model
Data Mining Data
Tools Warehouse
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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 Algorithm
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Site k Site 2 Site 1

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.
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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 processing
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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 ADDM
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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 Response
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10. MADM Systems-An Architectural
Approach contd….
Data Source
On Different
May 26, 2012
Sites 10

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 Execution
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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 signatures
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13. Security Measures ……
 Protecting the agent platform
 Sandboxing and safe code interpretation
 Proof carrying code
 Signed code
 Path histories

State appraisal
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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 Technology
May 26, 2012 14

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. 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
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 [11]Xining Li and Jingbo Ni. Deploying mobile agents in distributed data mining. Lecture Notes in
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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. Thank You
May 26, 2012 17