SCRS Conference Proceedings on Intelligent Systems
Ensemble based Effective Intrusion Detection System for Cloud Environment over UNSW-NB15 Dataset
Authors: Uzma Amin, Aamir S Ahanger, Faheem Masoodi and Alwi M Bamhdi
Publishing Date: 26-04-2022
ISBN: 978-93-91842-08-6
Abstract
Advanced computing innovations are rapidly evolving, resulting in the advent of new organizational and operational strategies. Cloud computing has emerged as one of the pre-eminent innovation in the recent years. Cloud computing enables its clients to access flexible, distributed computing domain via internet. Cloud has manifested itself as a viable framework that facilitates the use of application domains, data and infrastructural facilities that mainly encompasses workstations, network and storage infrastructure. Regardless of robust and comprehensive server processing capabilities in contrast to client’s processing capabilities and efficiency there are numerous security risks to the cloud from both outside and within the cloud that might exploit security flaws to cause damage. Traditional security measures have some flaws when it comes to completely shielding the networks and devices from increasingly advanced attacks. Consequently, it is all important to build an intrusion detection system to detect and prevent all kinds of intrusions in the cloud with high accuracy along with low false alarms. In this study we have suggested an anomaly-based intrusion detection system that employs ML algorithms for detection of unknown malicious attacks using an ensemble approach over the UNSW-NB15 dataset. The experimental output demonstrated the accuracy of 99.28% and 99.47% for random forest and bagging algorithms respectively.
Keywords
Cloud Environment, Intrusion, UNSW-NB15 Dataset.
Cite as
Uzma Amin, Aamir S Ahanger, Faheem Masoodi and Alwi M Bamhdi, "Ensemble based Effective Intrusion Detection System for Cloud Environment over UNSW-NB15 Dataset", In: Raju Pal and Praveen Kumar Shukla (eds), SCRS Conference Proceedings on Intelligent Systems, SCRS, India, 2022, pp. 483-494. https://doi.org/10.52458/978-93-91842-08-6-46