International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 6 Issue 5
September-October 2024
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Spectrogram Image Based Network Anomaly Detyection System using Deep Convolutional Neural Network
Author(s) | Mala M V, Kumaraswamy S |
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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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E-ISSN 2582-2160
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IJFMR DOI prefix is
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