Application of adversarial machine learning in network intrusion detection
In recent years, machine learning (ML) has become the mainstream network intrusion detection system(NIDS).However, the inherent vulnerabilities of machine learning make it difficult to resist adversarial attacks, which can mislead the models by adding subtle perturbations to the input sample.Adversa...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021193/ |
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| _version_ | 1841539197552295936 |
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| author | Qixu LIU Junnan WANG Jie YIN Yanhui CHEN Jiaxi LIU |
| author_facet | Qixu LIU Junnan WANG Jie YIN Yanhui CHEN Jiaxi LIU |
| author_sort | Qixu LIU |
| collection | DOAJ |
| description | In recent years, machine learning (ML) has become the mainstream network intrusion detection system(NIDS).However, the inherent vulnerabilities of machine learning make it difficult to resist adversarial attacks, which can mislead the models by adding subtle perturbations to the input sample.Adversarial machine learning (AML) has been extensively studied in image recognition.In the field of intrusion detection, which is inherently highly antagonistic, it may directly make ML-based detectors unavailable and cause significant property damage.To deal with such threats, the latest work of applying AML technology was systematically investigated in NIDS from two perspectives: attack and defense.First, the unique constraints and challenges were revealed when applying AML technology in the NIDS field; secondly, a multi-dimensional taxonomy was proposed according to the adversarial attack stage, and current work was compared and summarized on this basis; finally, the future research directions was discussed. |
| format | Article |
| id | doaj-art-604fe4b30ae743da96f7e3782905a81e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-604fe4b30ae743da96f7e3782905a81e2025-01-14T07:23:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-11-014211259745800Application of adversarial machine learning in network intrusion detectionQixu LIUJunnan WANGJie YINYanhui CHENJiaxi LIUIn recent years, machine learning (ML) has become the mainstream network intrusion detection system(NIDS).However, the inherent vulnerabilities of machine learning make it difficult to resist adversarial attacks, which can mislead the models by adding subtle perturbations to the input sample.Adversarial machine learning (AML) has been extensively studied in image recognition.In the field of intrusion detection, which is inherently highly antagonistic, it may directly make ML-based detectors unavailable and cause significant property damage.To deal with such threats, the latest work of applying AML technology was systematically investigated in NIDS from two perspectives: attack and defense.First, the unique constraints and challenges were revealed when applying AML technology in the NIDS field; secondly, a multi-dimensional taxonomy was proposed according to the adversarial attack stage, and current work was compared and summarized on this basis; finally, the future research directions was discussed.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021193/intrusion detectionmalicious trafficadversarial attackadversarial defense |
| spellingShingle | Qixu LIU Junnan WANG Jie YIN Yanhui CHEN Jiaxi LIU Application of adversarial machine learning in network intrusion detection Tongxin xuebao intrusion detection malicious traffic adversarial attack adversarial defense |
| title | Application of adversarial machine learning in network intrusion detection |
| title_full | Application of adversarial machine learning in network intrusion detection |
| title_fullStr | Application of adversarial machine learning in network intrusion detection |
| title_full_unstemmed | Application of adversarial machine learning in network intrusion detection |
| title_short | Application of adversarial machine learning in network intrusion detection |
| title_sort | application of adversarial machine learning in network intrusion detection |
| topic | intrusion detection malicious traffic adversarial attack adversarial defense |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021193/ |
| work_keys_str_mv | AT qixuliu applicationofadversarialmachinelearninginnetworkintrusiondetection AT junnanwang applicationofadversarialmachinelearninginnetworkintrusiondetection AT jieyin applicationofadversarialmachinelearninginnetworkintrusiondetection AT yanhuichen applicationofadversarialmachinelearninginnetworkintrusiondetection AT jiaxiliu applicationofadversarialmachinelearninginnetworkintrusiondetection |