NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion Article Swipe
Aritran Piplai
,
Sai Sree Laya Chukkapalli
,
Anupam Joshi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00020
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00020
With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attack, were detected using well-crafted rules. An attacker who has knowledge in the field of cyber-defence could make educated guesses to sometimes accurately predict which particular features of network traffic data the cyber-defence mechanism is looking at. With this information, the attacker can circumvent a rule-based cyber-defense system. However, after the advancements of machine learning for network anomaly, it is not easy for a human to understand how to bypass a cyber-defence system. Recently, adversarial attacks have become increasingly common to defeat machine learning algorithms. In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system. We propose a Generative Adversarial Network(GAN)based algorithm to generate data to train an efficient neural network based classifier, and we subsequently break the system using adversarial attacks.
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Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00020
- OA Status
- green
- Cited By
- 7
- References
- 16
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3008285109
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3008285109Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00020Digital Object Identifier
- Title
-
NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-01Full publication date if available
- Authors
-
Aritran Piplai, Sai Sree Laya Chukkapalli, Anupam JoshiList of authors in order
- Landing page
-
https://doi.org/10.1109/bigdatasecurity-hpsc-ids49724.2020.00020Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2002.08527Direct OA link when available
- Concepts
-
Adversarial system, Computer science, Adversarial machine learning, Artificial intelligence, Machine learning, Classifier (UML), Artificial neural network, Generative adversarial network, Generative grammar, Intrusion detection system, Deep learning, Anomaly detection, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6640425456, https://openalex.org/W6770149271, https://openalex.org/W2807487033, https://openalex.org/W2085305295, https://openalex.org/W2084496302, https://openalex.org/W2919021187, https://openalex.org/W6744095535, https://openalex.org/W2793412195, https://openalex.org/W2759471388, https://openalex.org/W2963207607, https://openalex.org/W2974581576, https://openalex.org/W2756182389, https://openalex.org/W2099471712, https://openalex.org/W2952451294, https://openalex.org/W2032585423, https://openalex.org/W2984419450 |
| referenced_works_count | 16 |
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