Deep Learning-Based Intrusion Detection With Adversaries
Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image classification domain, i.e., they leave some opportunities...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8408779/ |
| _version_ | 1818645084116090880 |
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| author | Zheng Wang |
| author_facet | Zheng Wang |
| author_sort | Zheng Wang |
| collection | DOAJ |
| description | Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image classification domain, i.e., they leave some opportunities for an attacker to fool the networks into misclassification by introducing imperceptible changes to the original pixels in an image. The vulnerability raises some concerns in applying deep neural networks in security-critical areas, such as intrusion detection. In this paper, we investigate the performances of the state-of-the-art attack algorithms against deep learning-based intrusion detection on the NSL-KDD data set. The vulnerabilities of neural networks employed by the intrusion detection systems are experimentally validated. The roles of individual features in generating adversarial examples are explored. Based on our findings, the feasibility and applicability of the attack methodologies are discussed. |
| first_indexed | 2024-12-17T00:25:07Z |
| format | Article |
| id | doaj.art-b0246d706d6d4758acaf70eadd19e877 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:25:07Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b0246d706d6d4758acaf70eadd19e8772022-12-21T22:10:28ZengIEEEIEEE Access2169-35362018-01-016383673838410.1109/ACCESS.2018.28545998408779Deep Learning-Based Intrusion Detection With AdversariesZheng Wang0https://orcid.org/0000-0003-2744-9345National Institute of Standards and Technology, Gaithersburg, MD, USADeep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image classification domain, i.e., they leave some opportunities for an attacker to fool the networks into misclassification by introducing imperceptible changes to the original pixels in an image. The vulnerability raises some concerns in applying deep neural networks in security-critical areas, such as intrusion detection. In this paper, we investigate the performances of the state-of-the-art attack algorithms against deep learning-based intrusion detection on the NSL-KDD data set. The vulnerabilities of neural networks employed by the intrusion detection systems are experimentally validated. The roles of individual features in generating adversarial examples are explored. Based on our findings, the feasibility and applicability of the attack methodologies are discussed.https://ieeexplore.ieee.org/document/8408779/Intrusion detectionneural networksclassification algorithmsdata security |
| spellingShingle | Zheng Wang Deep Learning-Based Intrusion Detection With Adversaries IEEE Access Intrusion detection neural networks classification algorithms data security |
| title | Deep Learning-Based Intrusion Detection With Adversaries |
| title_full | Deep Learning-Based Intrusion Detection With Adversaries |
| title_fullStr | Deep Learning-Based Intrusion Detection With Adversaries |
| title_full_unstemmed | Deep Learning-Based Intrusion Detection With Adversaries |
| title_short | Deep Learning-Based Intrusion Detection With Adversaries |
| title_sort | deep learning based intrusion detection with adversaries |
| topic | Intrusion detection neural networks classification algorithms data security |
| url | https://ieeexplore.ieee.org/document/8408779/ |
| work_keys_str_mv | AT zhengwang deeplearningbasedintrusiondetectionwithadversaries |