Relation Estimation of Packets Dropped by Wormhole Attack to Packets Sent Using Regression Analysis

Abstract

MANET is exposed to the possibility of being attacked or harmed by so many attacks like Black hole, Wormhole, Jellyfish, etc. The attacking nodes can easily set Wormhole attack by duplicating a route which is shorter than the original within network. In this paper, we estimate the pattern of relation between packets dropped to packets sent, that is packet delivery fraction (PDF) by Wormhole attack using regression analysis, a machine learning approach, by method of least square (MLS), and least absolute deviation (LAD). A comparative analysis between these two techniques is done. As there is high degree of linearity between packets sent and packets dropped by Wormhole attack, the pattern of dropped packet is proved by MLS regression and LAD regression. Accuracy is also tested. MLS and LAD regression algorithms are also developed to estimate this pattern for Wormhole attack. Wormhole attackers create a duplicate tunnel (wired link or high frequency) from source to destination. An illusion is created by this, that the distance through this tunnel from source to destination is minimal and must take less time. Comparing to this fake tunnel, the original route takes more time. So it is necessary to calculate the time taken to estimate the relation of packets dropped to packets sent by Wormhole attack. Better performance by linear regression analysis for pattern estimation of these dropped packets is proved by simulation, done by MATLAB simulator for Wormhole attack.