Biblio

This paper investigates the possibility of using ensemble learning methods to improve the performance of intrusion detection systems. We compare an ensemble of three ensemble learning methods, boosting, bagging and stacking in order to improve the detection rate and to reduce the false alarm rate. These ensemble methods use well-known and different base classification algorithms, J48 (decision tree), NB (Naïve Bayes), MLP (Neural Network) and REPTree. The comparison experiments are applied on UNSW-NB15 data set a recent public data set for network intrusion detection systems. Results show that using boosting, bagging can achieve higher accuracy than single classifier but stacking performs better than other ensemble learning methods.

Cloud computation has become prominent with seemingly unlimited amount of storage and computation available to users. Yet, security is a major issue that hampers the growth of cloud. In this research we investigate a collaborative Intrusion Detection System (IDS) based on the ensemble learning method. It uses weak classifiers, and allows the use of untapped resources of cloud to detect various types of attacks on the cloud system. In the proposed system, tasks are distributed among available virtual machines (VM), individual results are then merged for the final adaptation of the learning model. Performance evaluation is carried out using decision trees and using fuzzy classifiers, on KDD99, one of the largest datasets for IDS. Segmentation of the dataset is done in order to mimic the behavior of real-time data traffic occurred in a real cloud environment. The experimental results show that the proposed approach reduces the execution time with improved accuracy, and is fault-tolerant when handling VM failures. The system is a proof-of-concept model for a scalable, cloud-based distributed system that is able to explore untapped resources, and may be used as a base model for a real-time hierarchical IDS.