Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems
| Title | Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Usama, M., Asim, M., Latif, S., Qadir, J., Ala-Al-Fuqaha |
| Conference Name | 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC) |
| Date Published | June 2019 |
| Publisher | IEEE |
| ISBN Number | 978-1-5386-7747-6 |
| Keywords | adversarial attacks thwarting, Adversarial Machine Learning, adversarial ML attack, adversarial perturbations, adversary, anomalous traffic, composability, computer network security, feature extraction, Gallium nitride, gan, generative adversarial networks, IDS, intrusion detection system, malicious intrusions, malicious network traffic, Malware, ML models, ML-based IDS, network intrusion detection systems, network security suite, Perturbation methods, Probes, pubcrawl, resilience, Resiliency, Robustness |
| Abstract | Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend the network from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in the literature for the detection of malicious network traffic. However, recent works have shown that ML models are vulnerable to adversarial perturbations through which an adversary can cause IDSs to malfunction by introducing a small impracticable perturbation in the network traffic. In this paper, we propose an adversarial ML attack using generative adversarial networks (GANs) that can successfully evade an ML-based IDS. We also show that GANs can be used to inoculate the IDS and make it more robust to adversarial perturbations. |
| URL | https://ieeexplore.ieee.org/document/8766353 |
| DOI | 10.1109/IWCMC.2019.8766353 |
| Citation Key | usama_generative_2019 |
- intrusion detection system
- Robustness
- Resiliency
- resilience
- pubcrawl
- Probes
- Perturbation methods
- network security suite
- network intrusion detection systems
- ML-based IDS
- ML models
- malware
- malicious network traffic
- malicious intrusions
- adversarial attacks thwarting
- IDS
- generative adversarial networks
- gan
- Gallium nitride
- feature extraction
- computer network security
- composability
- anomalous traffic
- adversary
- adversarial perturbations
- adversarial ML attack
- Adversarial Machine Learning