- Title
- Effective multitask deep learning for IoT malware detection and identification using behavioral traffic analysis
- Creator
- Ali, Sajid; Abusabha, Omar; Ali, Farman; Imran, Muhammad; Abuhmed, Tamer
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/196169
- Identifier
- vital:18649
- Identifier
-
https://doi.org/10.1109/TNSM.2022.3200741
- Identifier
- ISSN:1932-4537 (ISSN)
- Abstract
- Despite the benefits of the Internet of Things (IoT), the growing influx of IoT-specific malware coordinating large-scale cyberattacks via infected IoT devices has created a substantial threat to the Internet ecosystem. Assessing IoT systems' security and developing mitigation measures to prevent the spread of IoT malware is therefore critical. Furthermore, for training and testing the fidelity of cyber security-based Machine Learning (ML) and Deep Learning (DL) approaches, the collection and exploration of information from multiple sources from the IoT are crucial. In this regard, we propose a multitask DL model for detecting IoT malware. Our proposed Long Short-Term Memory (LSTM) based model efficiently performs two tasks: 1) determination of whether the provided traffic is benign or malicious, and 2) determination of the malware type for identifying malicious network traffic. We used large-scale traffic data of 145. pcap files of benign and malicious traffic collected from 18 different IoT devices. We performed a time-series analysis on the packets of traffic flows, which were then used to train the proposed model. The features extracted from the dataset were categorized into three modalities: flow-related, traffic flag-related, and packet payload-related features. A feature selection approach was employed at the feature and modality levels, and the best modalities and features were utilized for performance enhancement. For tasks 1 and 2 and multitask classification, the flow-related and flag-related modalities showed the best testing accuracies of 92.63%, 88.45%, and 95.83%, respectively. © 2004-2012 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Network and Service Management Vol. 20, no. 2 (2023), p. 1199-1209
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2022 IEEE
- Subject
- 4006 Communications engineering; 4604 Cybersecurity and privacy; 4606 Distributed computing and systems software; Cybersecurity; Heterogeneity traffic analysis; IoT malware detection; Malware identification; Multimodal learning; Multitask deep learning
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