Visible to the public Collaborative Ensemble-Learning Based Intrusion Detection Systems for Clouds

TitleCollaborative Ensemble-Learning Based Intrusion Detection Systems for Clouds
Publication TypeConference Paper
Year of Publication2016
AuthorsMehetrey, P., Shahriari, B., Moh, M.
Conference Name2016 International Conference on Collaboration Technologies and Systems (CTS)
Keywordsanomaly detection, Bagging, cloud computing, cloud-based distributed system, Collaboration, collaborative ensemble-learning, Collaborative Systems, composability, dataset segmentation, Decision Tree, Decision trees, Ensemble Learning, fault tolerant computing, fault-tolerance, fuzzy classifier, fuzzy classifiers, groupware, IDS, Intrusion detection, Intrusion Detection Systems, intrusion tolerance, KDD99, learning (artificial intelligence), Learning systems, pattern classification, pubcrawl, Resiliency, security of data, Training, virtual machines, VM failures
AbstractCloud 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.
Citation Keymehetrey_collaborative_2016