LAUSR

Vampire attacks are considered to be the most vulnerable resource draining attack that is potential in disabling the connectivity of the network by draining mobile node’s energy at a faster rate. This vampire attack is generic as they exploit the characteristic features of the base protocol used for enabling communication in mobile ad hoc networks (MANETs). The core objective of this paper is an attempt to formulate an energy forecasting mechanism using grey theory that ensures reliable network connectivity that gets influenced through the vampire behaviour of mobile nodes under active communication. This Semi-Markov chain-based grey prediction-based mitigation (SMCGPM) is an enhanced Markov chain model that integrates the characteristic features of stochastic theory and grey theory for improving the efficacy in detecting a specific kind of vampire attack called as stretch attack. In this technique, the elucidated data from each mobile node are initially modeled based on Grey model. Then, the residual error is calculated between the forecasted and observed values of energy possessed by the mobile nodes based on their packet forwarding rates. SMCGPM has the capability of predicting the possible transition behaviour of mobile nodes through the estimated residual error derived from the Markov chain matrices. Simulation results confirm that SMCGPM is predominant than the baseline prediction schemes by facilitating an effective detection rate of 29% as they achieve correctness and accuracy in prediction through Semi-Markov chain stochastic properties inspired energy factor prediction.