Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices

Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices

Machine Learning based anomaly detection ap-proaches have long training and validation cycles. With IoT devices rapidly proliferating, training anomaly models on a per device basis is impractical. This work explores the "transfer-ability"of a pre-trained autoencoder model across devices of...

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Bibliographic Details
Main Authors: Unsub Shafiq, Muhammad Khuram Shahza, Muhammad Anwar Mohd Nor, Qaisar Shaheen, Muhammad Shiraz, Abdullah Gani
Format: Article
Language:English
English
Published: Hindawi Limited 2022
Subjects:
QA76.75-76.765 Computer software
Online Access:https://eprints.ums.edu.my/id/eprint/34207/2/Transfer%20learning%20auto-encoder%20neural%20networks%20for%20anomaly%20detection%20of%20DDoS%20generating%20IoT%20devices.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34207/1/Transfer%20Learning%20Auto-Encoder%20Neural%20Networks%20for%20Anomaly%20Detection%20of%20DDoS%20Generating%20IoT%20Devices.pdf

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https://eprints.ums.edu.my/id/eprint/34207/2/Transfer%20learning%20auto-encoder%20neural%20networks%20for%20anomaly%20detection%20of%20DDoS%20generating%20IoT%20devices.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34207/1/Transfer%20Learning%20Auto-Encoder%20Neural%20Networks%20for%20Anomaly%20Detection%20of%20DDoS%20Generating%20IoT%20Devices.pdf

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