The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Evasion Attacks against Machine Learning at Test Time
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“They demonstrated that DBA is substantially more persistent and stealthy on diverse datasets such as finance and image data.”
From Vulnerabilities in federated learning (2021) · cites this paper as [44]
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“However, only a few works [44] investigate the poisoning attacks in a distributed environment where multiple malicious clients with the same attack strive to include poisoned training samples into the training procedures.”
From Vulnerabilities in federated learning (2021) · cites this paper as [44]
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Topal, A. O., Mançellari, E., Gillet, T., & Leprévost, F. (2026) · Journal of Information and Telecommunication · doi:10.1080/24751839.2026.2657061
Adversarial Learning for Fraud Detection
Lunghi, D., Simitsis, A., & Bontempi, G. (2026) · Springer eBooks · doi:10.1007/978-3-032-18765-9_11
Identifying Malicious and Benign Prompts in AI Agent Evasion Attacks Using Machine Learning
Mahmud, E., Murshid, M. M., Barua, A., Nisa, N. T. B. N., Arnob, S. D., & Turja, M. T. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11546222
MADFACTs: A Meta-learning Augmented Defense Framework for Adversarial Cyber Techniques
Chalé, M. W., Cox, B., Weir, J. D., & Bastian, N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-22211-4_5
Hi-Fi Flow: Real-Time High-Granularity Flow Feature Extraction for Robust Network Monitoring
Bi, S., Zhou, L., Wang, T., & Hou, T. (2026) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn69566.2026.00052
Yao, L., Shao, S., & Hariri, S. (2026) · Springer eBooks · doi:10.1007/978-3-031-88574-7_31
Expert-Driven AI: Empowering Models with Agentic Adaptation and Transparent Explainability
Jambur, P. V., Sowmyarani, C. N., Rangaswamy, S., Dayananda, P., & Subramanya, K. N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19321-6_31
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Noise-Aware Misclassification Attack Detection in Collaborative DNN Inference
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Vanguard: Automated Multi-Scanner Malware Detection Framework with Intelligent Correlation Engine
Gaddam, O., Reddy, M. D. P., & Devi, S. V. S. G. (2026) · International Conference on Inventive Computation and Information Technologies (ICICIT) · doi:10.1109/icicit69063.2026.11634227
A Four-Axis Systematization of AI and Cybersecurity in the Era of Large Language Models
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Metadata
- Authors
- Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., & Roli, F.
- Affiliations
- University of Cagliari (IT)
- University of Potsdam (DE)
- University of Tübingen (DE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 387-402
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3103836116
- Cited by
- 890
- References
- 13
- Citations by year
- 2026: 35 · 2025: 103 · 2024: 131 · 2023: 123 · 2022: 126 · 2021: 95 · 2020: 98 · 2019: 98 · 2018: 37 · 2017: 26 · 2016: 7 · 2015: 5 · 2014: 4 · 2013: 1
- FWCI
- 3.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Keywords
- evasion attacks, adversarial learning, gradient-based attacks, malware detection, PDF documents, countermeasures
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.
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