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Supervised machine learning techniques for efficient network intrusion detection
Qatar University, Doha, Qatar.
Qatar University, Doha, Qatar.
Qatar University, Doha, Qatar.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
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2019 (English)In: Proceedings - International Conference on Computer Communications and Networks, ICCCN, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in telecommunication industry. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) are being widely deployed and utilized by end users, including many private as well as public organizations. Despite its wide-spread acceptance, security is still the biggest threat in cloud computing environments. Users of cloud services are under constant fear of data loss, security breaches, information theft and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning (ML) techniques. In this work, we explore applicability of two well-known machine learning approaches, which are, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to detect intrusions or anomalous behavior in the cloud environment. We have developed ML models using ANN and SVM techniques and have compared their performances. We have used UNSW-NB-15 dataset to train and test the models. In addition, we have performed feature engineering and parameter tuning to find out optimal set of features with maximum accuracy to reduce the training time and complexity of the ML models. We observe that with proper features set, SVM and ANN techniques have been able to achieve anomaly detection accuracy of 91% and 92% respectively, which is higher compared against that of the one achieved in the literature, with reduced number of features needed to train the models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019.
Keywords [en]
Artificial Neural Networks, Cloud Computing, Intrusion Detection, Support Vector Machines, Anomaly detection, Computer networks, Cost effectiveness, Machine learning, Neural networks, Platform as a Service (PaaS), Software as a service (SaaS), Statistical tests, Supervised learning, Telecommunication industry, Web services, Cloud computing environments, Feature engineerings, Learning-based methods, Machine learning approaches, Network intrusion detection, Security application, Supervised machine learning, Virtualized data centers, Infrastructure as a service (IaaS)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-75727DOI: 10.1109/ICCCN.2019.8847179Scopus ID: 2-s2.0-85073165460ISBN: 9781728118567 (print)OAI: oai:DiVA.org:kau-75727DiVA, id: diva2:1369698
Conference
28th International Conference on Computer Communications and Networks, ICCCN 2019, 29 July 2019 through 1 August 2019
Available from: 2019-11-12 Created: 2019-11-12 Last updated: 2019-11-13Bibliographically approved

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Kassler, AndreasBhamare, Deval

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