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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International Journal For Multidisciplinary Research