A lightweight intrusion detection framework using focal loss variational autoencoder for internet of things / Shapla Khanam
Internet of Things (IoT) generates imbalanced network traffic; thus, the connected objects in the IoT face security issues, including different and unknown attack types. Even though traditional learning-based techniques have been used for intrusion detection in IoT, the detection of low-frequency...
Saved in:
Main Author: | Shapla , Khanam |
---|---|
Format: | Thesis |
Published: |
2022
|
Subjects: | |
Online Access: | http://studentsrepo.um.edu.my/14779/1/Shapla_Khanam.pdf http://studentsrepo.um.edu.my/14779/2/Shapla_Khanam.pdf http://studentsrepo.um.edu.my/14779/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Towards an effective intrusion detection model using focal loss variational autoencoder for Internet of Things (IoT)
by: Khanam, Shapla, et al.
Published: (2022) -
Optimized deep autoencoder model for internet of things intruder detection
by: Lahasan, Badr, et al.
Published: (2022) -
A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things
by: Khanam, Shapla, et al.
Published: (2020) -
Intrusion detection with deep learning on internet of things heterogeneous network
by: Sharipuddin, S., et al.
Published: (2021) -
Classification of attention deficit hyperactivity disorder using variational autoencoder
by: A. Samah, Azurah, et al.
Published: (2021)