Gaussian versus uniform distribution for intrusion detection in wireless sensor networks

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GAUSSIAN VERSUS UNIFORM DISTRIBUTION FOR INTRUSION
DETECTION IN WIRELESS SENSOR NETWORKS
ABSTRACT:
In a Wireless Sensor Network (WSN), intrusion detection is of significant importance in many
applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a
battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment,
the detection probability is the same for any point in a WSN. However, some applications may
require different degrees of detection probability at different locations. For example, an intrusion
detection application may need improved detection probability around important entities.
Gaussian-distributed WSNs can provide differentiated detection capabilities at different locations
but related work is limited.
This paper analyzes the problem of intrusion detection in a Gaussian-distributed WSN by
characterizing the detection probability with respect to the application requirements and the
network parameters under both single-sensing detection and multiple-sensing detection
scenarios. Effects of different network parameters on the detection probability are examined in
detail. Furthermore, performance of Gaussian-distributed WSNs is compared with uniformly
distributed WSNs. This work allows us to analytically formulate detection probability in a
random WSN and provides guidelines in selecting an appropriate deployment strategy and
determining critical network parameters.
ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
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VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

Gaussian versus uniform distribution for intrusion detection in wireless sensor networks

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Wireless networks are been used now-a-days. The most paramount fact about wireless network is it is mobile. It is thus utilized in many fields. One of the most paramount applications of wireless networks is Mobile AdhocNETwork (MANET) in which that all the nodes work as both transmitter and receiver. MANETs are utilized in sundry fields like military, industry and emergency recuperation. So it is paramount to have a firsthand erudition about MANETs. But there is a certain drawback in MANETs, in which it becomes prone to malicious attacks very expeditious. To evade such attacks a good intrusion detection system is needed. In this paper, we have proposed a system which can recognize and additionally keep the malicious attacks. The system is named as Enhanced Adaptive Acknowledgments (EAACK). EAACK gives a superior malicious attack than the standard strategies.

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Gaussian versus uniform distribution for intrusion detection in wireless sensor networks

1. GAUSSIAN VERSUS UNIFORM DISTRIBUTION FOR INTRUSION DETECTION IN WIRELESS SENSOR NETWORKS ABSTRACT: In a Wireless Sensor Network (WSN), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment, the detection probability is the same for any point in a WSN. However, some applications may require different degrees of detection probability at different locations. For example, an intrusion detection application may need improved detection probability around important entities. Gaussian-distributed WSNs can provide differentiated detection capabilities at different locations but related work is limited. This paper analyzes the problem of intrusion detection in a Gaussian-distributed WSN by characterizing the detection probability with respect to the application requirements and the network parameters under both single-sensing detection and multiple-sensing detection scenarios. Effects of different network parameters on the detection probability are examined in detail. Furthermore, performance of Gaussian-distributed WSNs is compared with uniformly distributed WSNs. This work allows us to analytically formulate detection probability in a random WSN and provides guidelines in selecting an appropriate deployment strategy and determining critical network parameters. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

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