Intrusion Detection System with a Modified DASO Optimization Algorithm

Abstract

The Dolphin Atom Search Optimization (DASO) is modified in the proposed work. The Bayesian information gain model has the naïve-bayes classifier centered on the parameters, which include Information Gain (IG), Class-wide Information Gain (CIG), and mutual information. The information is fed to the ID phase for processing at the Deep RNN classifier and the performance is tested with the developed proposed algorithm. An optimization algorithm proposed, is based on the ‘Bayesian information gain model’ to tune the weights in order to generate effective detection decisions through the fitness measure. The updated information at the selection segment is processed. The performance is outlayed on the general metrics including accuracy, sensitivity, and selectivity and it is better than the results with the existing algorithms. Enhancement performance is observed with the machine learning and Deep Recurrent Neural Network techniques. The proposed DASO is developed by integrating the Dolphin Echolocation (DE) with the Atom Search Optimization (ASO).