Research Area: Cloud Security
In this paper, an approach to protecting virtual machines (VMs) against denial of service (DoS) attacks in a cloud environment is proposed. An open source cloud computing platform (Eucalyptus) has been deployed, and experimentation was carried out on this setup. We investigate attacks emanating from one or more virtual machines (VMs) to another VM in a multi-tenancy cloud environment. Various types of DoS attacks are mounted on a webserver VM. To detect such attacks from a cloud provider-s perspective, an intrusion detection system (IDS) is needed. In this research we propose and implement an IDS which incorporates a packet sniffer, feature extractor and a classifier as part of its design. We have experimented with the one-class support vector machines (SVM) algorithm for classification of the attacks. The dataset containing time-based traffic flow features is passed through the classifier to detect the attack traffic from legitimate traffic. The proposed IDS design shows promising results in being able to detect the ICMP Flood, Ping-of-Death, UDP Flood, TCP SYN Flood, TCP LAND and DNS Flood attacks with high classification accuracies.
Keywords:
Eucalyptus Cloud
Denial of Service
Intrusion Detection System
One-Class Support Vector Machines
Virtual Machines
Author(s) Name: Raneel Kumar; Sunil Pranit Lal; Alok Sharma
Journal name:
Conferrence name: 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)
Publisher name: IEEE
DOI: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.70
Volume Information:
