The document presents an overview of multi-agent based decentralized knowledge discovery and agent security. It discusses data mining versus decentralized data mining and the use of agents for distributed data mining. It describes the basic components of agent-based distributed data mining systems including the application layer, data mining layer, and agent grid infrastructure layer. It also discusses multi-agent distributed data mining systems, the roles of different agent types, security issues for agents, and measures to secure agents. Finally, it outlines potential future applications and provides references for further reading.

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
 [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. Thank You
May 26, 2012 17