6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

An intrusion detection framework for Sensor Networks using Honeypot and Swarm Intelligence

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  • @INPROCEEDINGS{10.4108/ICST.MOBIQUITOUS2009.7084,
        author={Rajani  Muraleedharan and Lisa Ann  Osadciw},
        title={An intrusion detection framework for Sensor Networks using Honeypot and Swarm Intelligence},
        proceedings={6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={IEEE},
        proceedings_a={MOBIQUITOUS},
        year={2009},
        month={11},
        keywords={Denial of Service Honeypot Intrusion Detection  Security Swarm Intelligence Wireless Sensor Network.},
        doi={10.4108/ICST.MOBIQUITOUS2009.7084}
    }
    
  • Rajani Muraleedharan
    Lisa Ann Osadciw
    Year: 2009
    An intrusion detection framework for Sensor Networks using Honeypot and Swarm Intelligence
    MOBIQUITOUS
    IEEE
    DOI: 10.4108/ICST.MOBIQUITOUS2009.7084
Rajani Muraleedharan1,*, Lisa Ann Osadciw1,*
  • 1: Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY- 13244-1240, Phone: 315-443-3366/Fax: 315-443-2583.
*Contact email: rmuralee@ecs.syr.edu, laosadci@ecs.syr.edu

Abstract

Wireless sensor networks have become a technology for the new millennium with the endless possibilities for applications ranging from academic to military. These tiny sensors are deployed in open environments, where security for data or hardware cannot be guaranteed. Unfortunately due to the resource constraints, traditional security schemes cannot be applied. Therefore designing protocols that can operate securely using smart inherent features is the best option. In this paper, an efficient way of detecting an intruder using Honeypot and swarm intelligence is proposed. The Honeypot architecture strategically enables agents to track the intruders. This process of locating an intruder reduces the false alarm detection rate caused by denial-of-service attacks. A detailed analysis of the attack is captured to predict future attacks using pattern recognition. The proposed framework is evaluated based on accuracy and speed of intruder detection before the network is compromised. This process of detecting the intruder earlier helps learn his/her future attacks, but also a defensive countermeasure.