Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)

A New Combined Model with Reduced Label Dependency for Malware Classification

Authors
Prishita Ray, Tanmayi Nandan, Lahari Anne, Kakelli Anil Kumar
Corresponding Author
Prishita Ray
Available Online 13 September 2021.
DOI
https://doi.org/10.2991/ahis.k.210913.004How to use a DOI?
Keywords
Autoencoders, Computer security, Feature selection, FSFC, Ladder networks, Machine learning, Multi-class classification, Network intrusion malware
Abstract

With the technological advancements in recent times, security threats caused by malware are increasing with no bounds. The first step performed by security analysts for the detection and mitigation of malware is its classification. This paper aims to classify network intrusion malware using new-age machine learning techniques with reduced label dependency and identifies the most effective combination of feature selection and classification technique for this purpose. The proposed model, L2 Regularized Autoencoder Enabled Ladder Networks Classifier (RAELN-Classifier), is developed based on a combinatory analysis of various feature selection techniques like FSFC, variants of autoencoders and semi-supervised classification techniques such as ladder networks. The model is trained and tested over UNSW-NB15 and benchmark NSL-KDD datasets for accurate real time model performance evaluation using overall accuracy as well as per-class accuracy and was found to result in higher accuracy compared to similar baseline and state-of-the-art models.

Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
Series
Atlantis Highlights in Computer Sciences
Publication Date
13 September 2021
ISBN
978-94-6239-428-5
ISSN
2589-4900
DOI
https://doi.org/10.2991/ahis.k.210913.004How to use a DOI?
Copyright
© 2021, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Prishita Ray
AU  - Tanmayi Nandan
AU  - Lahari Anne
AU  - Kakelli Anil Kumar
PY  - 2021
DA  - 2021/09/13
TI  - A New Combined Model with Reduced Label Dependency for Malware Classification
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 23
EP  - 32
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.004
DO  - https://doi.org/10.2991/ahis.k.210913.004
ID  - Ray2021
ER  -