AI for Security and Security for AI
Abstract
On one side, the security industry has successfully adopted some AI-based techniques. Use varies from mitigating denial of service attacks, forensics, intrusion detection systems, homeland security, critical infrastructures protection, sensitive information leakage, access control, and malware detection. On the other side, we see the rise of Adversarial AI. Here the core idea is to subvert AI systems for fun and profit. The methods utilized for the production of AI systems are systematically vulnerable to a new class of vulnerabilities. Adversaries are exploiting these vulnerabilities to alter AI system behavior to serve a malicious end goal. This panel discusses some of these aspects.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Fine Tuning involves customizing pre-trained models to specific domains or user preferences, enhancing accuracy and relevance for niche applications.
“Fine Tuning: Involves customizing pre-trained models to specific domains or user preferences, enhancing accuracy and relevance for niche applications [60], [258], [259].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [259]
Cited by
Sorted by citations
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“• Data Security: In AI data security, key requirements and challenges include ensuring data confidentiality, adhering to consent norms, and safeguarding against vulnerabilities like membership inference attacks [228], [229].”
Background
- Authors
- Bertino, E., Kantarcioglu, M., Akcora, C. G., Samtani, S., Mittal, S., & Gupta, M.
- Affiliations
- Purdue University West Lafayette (US)
- The University of Texas at Dallas, Richardson, TX, USA
- University of Manitoba, Winnipeg, MAN, Canada
- Indiana University Bloomington (US)
- University of North Carolina Wilmington (US)
- Tennessee Technological University (US)
- Venue
- Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy
- Publisher
- ACM
- Published
- 2021-04-26
- Pages
- 333-334
- OpenAlex
- W3152764592
- Cited by
- 70
- References
- 0
- Citations by year
- 2026: 2 · 2025: 18 · 2024: 22 · 2023: 15 · 2022: 10 · 2021: 3
- FWCI
- 5.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Data and Application Security and Privacy (CODASPY) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- AI security, adversarial AI, adversarial vulnerability, AI-based intrusion detection, AI forensics, denial-of-service mitigation, malware detection, access control, critical infrastructure protection, homeland security, information leakage
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
