Enhanced Detection System for Trust Aware P2P Communication Networks

Abstract

Botnet is a number of computers that have been set up to forward transmissions to other computers unknowingly to the user of the system and it is most significant to detect the botnets. However, peer-to-peer (P2P) structured botnets are very difficult to detect because, it doesn't have any centralized server. In this paper, we deliver an infrastructure of P2P that will improve the trust of the peers and its data. In order to identify the botnets we provide a technique called data provenance integrity. It will ensure the correct origin or source of information and prevents opponents from using host resources. A reputation based trust model is used for selecting the trusted peer. In this model, each peer has a reputation value which is calculated based on its past activity. Here a hash table is used for efficient file searching and data stored in it is based on the reputation value.

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

“…EEG is preferred due to its low cost and high temporal resolution [10][11][12][13]. Since it is hard to localize the actual neural sources underneath due to unwanted noises, experimental limitations and overlapping of neural activities of other lobes of the brain [14][15][16][17]. To address this issue, a novel MSP inversionbased Bayesian framework algorithm with irregular patches that give the optimal free energy and minimum location in the visual cortex is proposed to solve EEG ill-posed inverse problems [18,19].…”