The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
Open accessAbstract
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Evasion Attacks against Machine Learning at Test Time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40994-3_25 · Cited by 890
Self-citation
Cited by
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
Kim, T., Kang, B., Rho, M., Sezer, S., & Im, E. G. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2866319 · Cited by 518
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
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Chen, X., Li, C., Wang, D., Wen, S., Zhang, J., Nepal, S., Xiang, Y., & Ren, K. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2932228 · Cited by 328
The relationship between trust in AI and trustworthy machine learning technologies
Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C. G., & van Moorsel, A. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372834 · Cited by 296
Metadata
- Authors
- Demontis, A., Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., & Roli, F.
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2019-07-01
- Volume
- 16
- Issue
- 4
- Pages
- 711-724
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W2611170103
- Cited by
- 288
- References
- 6
- FWCI
- 27.32
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. https://app.cochat.ai/papers/10.1109/tdsc.2017.2700270
- The paper
- Demontis, A., Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., & Roli, F. (2019). Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection. IEEE Transactions on Dependable and Secure Computing. https://doi.org/10.1109/tdsc.2017.2700270
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
