The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
On the feasibility of online malware detection with performance counters
Abstract
The proliferation of computers in any domain is followed by the proliferation of malware in that domain. Systems, including the latest mobile platforms, are laden with viruses, rootkits, spyware, adware and other classes of malware. Despite the existence of anti-virus software, malware threats persist and are growing as there exist a myriad of ways to subvert anti-virus (AV) software. In fact, attackers today exploit bugs in the AV software to break into systems.
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Citations
Cites
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A data mining framework for building intrusion detection models
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Self-citationBefore we knew it: an empirical study of zero-day attacks in the real world
Bilge, L., & Dumitraş, T. (2012) · Proceedings of the 2012 ACM conference on Computer and communications security · doi:10.1145/2382196.2382284 · Cited by 627
Scalable, behavior-based malware clustering
Bayer, U. (2009) · Cited by 603
Learning and Classification of Malware Behavior
Rieck, K., Holz, T., Willems, C., Düssel, P., & Laskov, P. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-70542-0_6 · Cited by 589
Automated Classification and Analysis of Internet Malware
Bailey, M., Oberheide, J., Andersen, J. H., Mao, Z. M., Jahanian, F., & Nazario, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74320-0_10 · Cited by 531
Mining specifications of malicious behavior
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Cited by
Security and privacy in smart farming: challenges and opportunities
Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020) · IEEE Access · doi:10.1109/access.2020.2975142 · Cited by 609
Ganju, K., Wang, Q., Yang, W., Gunter, C. A., & Borisov, N. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243834 · Cited by 475
Fog computing security: A review of current applications and security solutions
Khan, S., Parkinson, S., & Qin, Y. (2017) · Journal of Cloud Computing · doi:10.1186/s13677-017-0090-3 · Cited by 409
The Cybersecurity Landscape in Industrial Control Systems
McLaughlin, S., Konstantinou, C., Wang, X., Davi, L., Sadeghi, A.-R., Maniatakos, M., & Karri, R. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2512235 · Cited by 391
Malware Guard Extension: Using SGX to Conceal Cache Attacks
Schwarz, M., Weiser, S., Gruss, D., Maurice, C., & Mangard, S. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-60876-1_1 · Cited by 339
Metadata
- Authors
- Demme, J., Maycock, M., Schmitz, J., Tang, A., Waksman, A., Sethumadhavan, S., & Stolfo, S.
- Venue
- Proceedings of the 40th Annual International Symposium on Computer Architecture
- Publisher
- ACM
- Published
- 2013-06-23
- Pages
- 559-570
- ISSN
- 1063-6897 (ISSN-L)
- OpenAlex
- W2166844173
- Cited by
- 334
- References
- 15
- FWCI
- 14.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: On the feasibility of online malware detection with performance counters. https://app.cochat.ai/papers/10.1145/2485922.2485970
- The paper
- Demme, J., Maycock, M., Schmitz, J., Tang, A., Waksman, A., Sethumadhavan, S., & Stolfo, S. (2013). On the feasibility of online malware detection with performance counters. Proceedings of the 40th Annual International Symposium on Computer Architecture. https://doi.org/10.1145/2485922.2485970
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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.
