April 8, 2023 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

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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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Introduction

Data Mining-KDD De-Centralized Environment De-Centralized KDD(DDM) An Agent Multi Agent System

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Data Mining

Data Warehouse

Data Mining Tools

General Data Mining Model

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Distributed Data MiningDe-Centralized Data Mining model

Data Mining Algorithm

Data Mining Algorithm

Data Mining Algorithm

Site 1Site 2Site k

Local Model

Local Model

Local Model

Local Model Aggregation Final Model

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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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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

Agent Based DDM

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Basic Components of ADDM An ADDM system can be generalized into a set of

components

Fig. Overview of ADDM

Agent Grid Infrastructure Layer

Data Mining Layer

Application Layer

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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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MADM Systems-An Architectural Approach contd….

Data Source On Different Sites

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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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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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Security Measures ……

Protecting the agent platform

Sandboxing and safe code interpretation Proof carrying code Signed code Path histories

State appraisal

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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

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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.

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References [1] IJRIC ISSN: 2076-3328 www.ijric.org E-ISSN: 2076-3336 “. Agent based distributed data

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Intelligent Information Agents - The AgentLink Perspective. Lecture Notes in Computer Science 2586 Springer 2003.

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[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 .

[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

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Thank You