The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detecting crypto-ransomware in IoT networks based on energy consumption footprint
Open accessAbstract
An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure.
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
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332 · cited 2×
“Similar to the approach in (Buczak and Guven 2016), we use the following four common performance indicators for malware detection:”
Method
Cited by
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-citationSecuring internet of medical things systems: limitations, issues and recommendations
Yaacoub, J.-P. A., Noura, M., Noura, H. N., Salman, O., Yaacoub, E., Couturier, R., & Chehab, A. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.12.028 · Cited by 308
A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions
Oz, H., Aris, A., Levi, A., & Uluagac, A. S. (2022) · ACM Computing Surveys · doi:10.1145/3514229 · Cited by 297
Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
Kim, J., Shim, M., Hong, S., Shin, Y., & Choi, E. (2020) · Applied Sciences · doi:10.3390/app10197009 · Cited by 106
Metadata
- Authors
- Azmoodeh, A., Dehghantanha, A., Conti, M., & Choo, K.-K. R.
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2017-08-23
- Volume
- 9
- Issue
- 4
- Pages
- 1141-1152
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- OpenAlex
- W2745390745
- Cited by
- 293
- References
- 3
- FWCI
- 28.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Detecting crypto-ransomware in IoT networks based on energy consumption footprint. https://app.cochat.ai/papers/10.1007/s12652-017-0558-5
- The paper
- Azmoodeh, A., Dehghantanha, A., Conti, M., & Choo, K.-K. R. (2018). Detecting crypto-ransomware in IoT networks based on energy consumption footprint. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-017-0558-5
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.
