The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
Open accessAbstract
Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g., medical devices and wearable combat uniforms). These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g., to facilitate evaluation of future malware detection approaches).
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A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
HaddadPajouh, H., Dehghantanha, A., Khayami, R., & Choo, K.-K. R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.007 · Cited by 445
Self-citationInternet of Things attack detection using hybrid Deep Learning Model
Sahu, A. K., Sharma, S., Tanveer, M., & Raja, R. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.05.024 · Cited by 258
Pundir, S., Wazid, M., Singh, D. P., Das, A. K., Rodrigues, J. J. P. C., & Park, Y. (2020) · IEEE Access · doi:10.1109/access.2019.2962829 · Cited by 202
IoMT malware detection approaches: analysis and research challenges
Wazid, M., Das, A. K., Rodrigues, J. J. P. C., Shetty, S., & Park, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2960412 · Cited by 200
Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
Tobore, I., Li, J., Yuhang, L., Al-Handarish, Y., Kandwal, A., Nie, Z., & Wang, L. (2019) · JMIR mHealth and uHealth · doi:10.2196/11966 · Cited by 180
Metadata
- Authors
- Azmoodeh, A., Dehghantanha, A., & Choo, K.-K. R.
- Venue
- IEEE Transactions on Sustainable Computing
- Published
- 2019-01-01
- Volume
- 4
- Issue
- 1
- Pages
- 88-95
- ISSN
- 2377-3782 (ISSN-L)
- 2377-3790
- OpenAlex
- W2792450155
- Cited by
- 378
- References
- 5
- FWCI
- 37.74
- Type
- journal article
- Language
- en
- Open access
- green
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- Azmoodeh, A., Dehghantanha, A., & Choo, K.-K. R. (2019). Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning. IEEE Transactions on Sustainable Computing. https://doi.org/10.1109/tsusc.2018.2809665
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