The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
Open accessAbstract
Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated.
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Cites
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Towards Evaluating the Robustness of Neural Networks
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Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv · doi:10.48550/arxiv.1706.06083 · Cited by 6,602
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
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The Limitations of Deep Learning in Adversarial Settings
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Practical Black-Box Attacks against Machine Learning
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Cited by
The Role of Machine Learning in Cybersecurity
Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Brdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2023) · Digital Threats: Research and Practice · doi:10.1145/3545574 · Cited by 279
Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks
Apruzzese, G., Andreolini, M., Marchetti, M., Venturi, A., & Colajanni, M. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3031843 · Cited by 117
Metadata
- Authors
- Martins, N., Cruz, J. M., Cruz, T., & Henriques Abreu, P.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 35403-35419
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3007481080
- Cited by
- 213
- References
- 12
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- gold
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