International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 5 September-October 2024 Submit your research before last 3 days of October to publish your research paper in the issue of September-October.

Spectrogram Image Based Network Anomaly Detyection System using Deep Convolutional Neural Network

Author(s) Mala M V, Kumaraswamy S
Country India
Abstract The growth of the internet of things (IOT) generates new processing, networking infrastructure, data storage, and management capabilities. This massive data volume may be used to provide high-value information for decision support, forecasting, business intelligence, data-intensive science research, etc. Hence, the increasing frequency and potency of recent attacks and the constantly evolving attack vectors necessitate the development of improved detection approaches. The proposed ensemble multi binary attack model (EMBAM) is an Intrusion Detection System (IDS) that offers a unique anomaly-based IDS to detect normal behavior and abnormal attack(s), e.g., threats in a network. The EMBAM ensemble multiple binary classifiers into a single model by stacking.
Keywords Intrusion detection, EMBAM, Security.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-06
Cite This Spectrogram Image Based Network Anomaly Detyection System using Deep Convolutional Neural Network - Mala M V, Kumaraswamy S - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14403
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14403
Short DOI https://doi.org/gtp8n2

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