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Data-driven Intrusion Detection System for Small and Medium Enterprises

Elezaj, Ogerta; Yildirim Yayilgan, Sule; Abomhara, Mohamed Ali Saleh; Yeng, Prosper; Ahmed, Javed
Journal article, Peer reviewed
Accepted version
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Elezaj (3.162Mb)
URI
http://hdl.handle.net/11250/2627244
Date
2019
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  • Institutt for informasjonssikkerhet og kommunikasjonsteknologi [1558]
  • Publikasjoner fra CRIStin - NTNU [19694]
Original version
10.1109/CAMAD.2019.8858166
Abstract
Small and Medium Enterprises (SMEs) have become targets of attack by cyber criminals in resent times. This paper therefore aim to address awareness and challenges of SMEs related to IDSs as the most important defense tool against sophisticated and ever-growing network attacks. An IDSs framework was actually introduced for efficient network anomaly detection for SMEs and provided experimental results to illustrate the benefits of the proposed framework. The proposed framework deals with one of the main challenges that IDSs of SMEs are facing, the lack of scalability and autonomic self-adaptation. Training, testing and evaluation of IDSs applying different machine learning (ML) techniques are presented. Results of experiments show that using feature selection approaches can lead to better classification accuracy and improved computational speed.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD

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