Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications
Conference paper
Chowdhury, A., Isalm, M., Kaisar, S., Naha, R., Khoshkholghi, A., Aiash, M. and Khoda, M.E. 2023. Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications. IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications. Exeter, United Kingdom 01 - 03 Nov 2023 IEEE. pp. 2216-2221
Type | Conference paper |
---|---|
Title | Leveraging oversampling techniques in machine learning models for multi-class malware detection in smart home applications |
Authors | Chowdhury, A., Isalm, M., Kaisar, S., Naha, R., Khoshkholghi, A., Aiash, M. and Khoda, M.E. |
Abstract | Smarthome applications are becoming increasingly popular due to their ability to provide safety, comfort, and remote assistance. These applications are usually controlled using a smart home controller, which is often the target of malware attacks. A successful attack may result in financial loss, disclosure of personal and/or sensitive information, or even loss of human lives. Although machine learning models have been used in existing research for detecting multi-class malware attacks in smart home systems, they did not explicitly address the class imbalance problem in such cases. In addition, the use of ensemble learner is expected to provide improved performance. To address this, we investigated different oversampling techniques to increase the number of samples in the minority classes and incorporated ensemble learners to see their impact on the prediction performance. Experimental evaluation shows significant improvements (4-5%) in terms of accuracy, precision, recall, and F-1 score. Index Terms—Oversampling Techniques, Ensemble Models, Multi-class Malware Detection |
Keywords | Oversampling Techniques; Ensemble Models; Multi-class Malware Detection |
Sustainable Development Goals | 8 Decent work and economic growth |
Middlesex University Theme | Sustainability |
Conference | IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications |
Page range | 2216-2221 |
Proceedings Title | 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
ISSN | 2324-898X |
Electronic | 2324-9013 |
ISBN | |
Paperback | 9798350382006 |
Electronic | 9798350381993 |
Publisher | IEEE |
Publication dates | |
01 Nov 2023 | |
Online | 29 May 2024 |
Publication process dates | |
Accepted | 24 Sep 2023 |
Deposited | 13 Oct 2023 |
Output status | Published |
Accepted author manuscript | File Access Level Open |
Copyright Statement | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/xpl/conhome/10538469/proceeding |
Language | English |
https://repository.mdx.ac.uk/item/v8q5x
Download files
174
total views10
total downloads2
views this month2
downloads this month