MANET Mining: Mining Temporal Association Rules

Jabas, Ahmad; Garimella, R.M.; Ramachandram, S.

doi:10.1109/ispa.2008.66

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Thus, the Ad hoc networks will be free from any malicious activity and normal flow of packets will be possible. [8] The objective is to implement a smart intrusion detection system using a Data Mining technique in MANET. Smart means the intrusion system should be able to identify the nodes which can impose threat to the network formed and should take steps in advance so that the attack before its impact should be controlled or managed by the IDS.…”

Section: Discussionmentioning

confidence: 99%

Smart intrusion detection system for MANET

Kashyap

2015

2015 International Conference on Advances in Computer Engineering and Applications

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Mobile Ad hoc networks (MANET) are infrastructure less networks which consist of self organized & self configured multihop nodes. The topology of these networks change with time. The nodes in the network not only act as routers but also as hosts. Two main issues in MANET are challenging namely, optimized routing and security. The approach followed in this paper suggests use of data mining techniques such as clustering and classification in developing intrusion detection system for MANET. We will use Zone routing protocol (ZRP) for packet flow which is hybrid in nature. Then various properties of the malicious, selfish and loyal nodes are used to identify the cluster heads. Cluster head is one of the loyal nodes which are having sufficient energy to transmit the message in the mobile Ad hoc networks and also guarantees successful transmission of data from source to destination.

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Section: Discussionmentioning

confidence: 99%

Smart intrusion detection system for MANET

Kashyap

2015

2015 International Conference on Advances in Computer Engineering and Applications

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“…Hu [13] applied the mining technique for composite web service discovery but his work was not applied to Mobile Ad hoc Network. Jabas [14] applies the mining technique to MANET traffic to find interesting relations ships among MANET nodes. Their research was motivated by the fact that these relationships can assist routing, MAC and other protocols of MANET.…”

Section: Related Workmentioning

confidence: 99%

Approach of Association Rules Mining for Service Discovery in Mobile Adhoc Network

Bhole¹,

Ranade²

2012

IJCA

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The Service Discovery in Mobile Ad hoc Networks (MANET) is a difficult task because of the changing nature of such networks. Different services are available in MANET considering time and location. Many varying traditional solutions to service discovery of Internet are not well suited for MANET because of their Ad hoc nature. Consequently, Service Discovery (SD) in the network a perquisite for efficient usage of network resources is a complex problem. In this work Association Rules mining algorithm are used to get service discovery. Using the correlation among the services and piggybacking future service request answers along with current service requests, there is significant gain in performance. Two algorithms of association rules mining are used for discovering the services in MANET. FPTree algorithm is already been used for service discovery. We have approached towards Apriori algorithm for service discovery in MANET with the significant gain in performance.

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“…Hu [16] applied the mining technique for composite web service discovery but this work was not applied to Mobile Ad hoc Network. Jabas [17] applies the mining technique to MANET traffic to find interesting relations ships among MANET nodes. Their research was motivated by the fact that these relationships can assist routing, MAC and other protocols of MANET.…”

Section: Literature Reviewmentioning

confidence: 99%

Service discovery in Mobile Ad hoc networks using Association Rules Mining

Islam

Shaikh

2009

2009 IEEE 13th International Multitopic Conference

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Abstract-Service Discovery in Mobile Ad hoc Networks (MANET) is a difficult task because of the dynamic nature of such networks. The availability of the services in MANET varies with respect to time and location. However, the service discovery requests issued by a consumer in one session are correlated, in most of the cases. If we can find out the correlation among the services and piggyback one service response with others in advance, we can gain improvement in performance. By utilizing this correlation property among service discovery requests, we have proposed a novel approach to Service Discovery in Mobile Ad hoc Networks. We have simulated the proposed approach in JIST/SWANS simulator and the results have shown significant performance improvement.

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MANET Mining: Mining Temporal Association Rules

Cited by 5 publications

References 3 publications

Smart intrusion detection system for MANET

Smart intrusion detection system for MANET

Approach of Association Rules Mining for Service Discovery in Mobile Adhoc Network

Service discovery in Mobile Ad hoc networks using Association Rules Mining

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