Feature selection using PRACO method for IDS in cloud environment

Abstract

Cloud Computing is the distribution of computing resources on demand to the users over Internet. But with virtual existence of data and resources comes the problem of privacy and security. In such environments Intrusion Detection System (IDS) comes in handy. They read huge chunks of data to find out attack patterns. But learning through this huge amount of data is very time consuming. So, data reduction is necessary. Using feature selection methods, number of features can be reduced by eliminating redundant and irrelevant attributes from datasets. In this paper the authors have proposed a Penalty Reward based Ant Colony Optimization (PRACO) method for feature selection. The penalty and reward terms used in this paper help in better exploration-exploitation trade-off by rewarding the useful features and penalizing the other ones. Along with that the concepts of max-relevance and min-redundancy are also used to indicate interactions between selected features. The proposed model is assessed on 10% KDD Cup 99, NSL-KDD and UNSW-NB15 datasets. It was observed that the PRACO method achieved 81.682% and 83.584% accuracy on average during train-test phase using NSL-KDD and UNSW-NB15 datasets. The results provide substantial evidence that the proposed model is effective in finding optimal results and thus provide IDS with increased efficiency.