Adversarial Robustness in Hybrid Quantum-Classical Deep Learning for Botnet DGA Detection

• Item type: Journal(1)
• 公開日: 2022-09-15
• タイトル: Adversarial Robustness in Hybrid Quantum-Classical Deep Learning for Botnet DGA Detection
• タイトル / 言語: en
• タイトル: Adversarial Robustness in Hybrid Quantum-Classical Deep Learning for Botnet DGA Detection
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:量子時代をみすえたコンピュータセキュリティ技術] adversarial defense, adversarial ML, adversarial training, computer security, cybersecurity, quantum adversarial machine learning, quantum computing, quantum deep learning
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Science and Technology, Kumamoto University／Department of Information Technology, Faculty of Intelligent Electrical and Informatics Technology (F-ELECTICS), Institut Teknologi Sepuluh Nopember
• 著者所属: Center for Management of Information Technologies, Kumamoto University
• 著者所属: Faculty of Advanced Science and Technology, Kumamoto University
• 著者所属: Center for Management of Information Technologies, Kumamoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Kumamoto University / Department of Information Technology, Faculty of Intelligent Electrical and Informatics Technology (F-ELECTICS), Institut Teknologi Sepuluh Nopember
• 著者所属(英): en
• 著者所属(英): Center for Management of Information Technologies, Kumamoto University
• 著者所属(英): en
• 著者所属(英): Faculty of Advanced Science and Technology, Kumamoto University
• 著者所属(英): en
• 著者所属(英): Center for Management of Information Technologies, Kumamoto University
• 著者名: Hatma, Suryotrisongko; Yasuo, Musashi; Akio, Tsuneda; Kenichi, Sugitani
• 著者名(英): Hatma, Suryotrisongko; Yasuo, Musashi; Akio, Tsuneda; Kenichi, Sugitani
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper aims to contribute to the adversarial defense research gap in the current state-of-the-art of adversarial machine learning (ML) attacks and defense. More specifically, it contributes to the metric measurement of the robustness of artificial intelligence (AI)/ML models against adversarial example attacks, which currently remains an open question in the cybersecurity domain and to an even greater extent for quantum computing-based AI/ML applications. We propose a new adversarial robustness measurement approach which measures the statistical properties (such as the average of the accuracies and t-test results) from the performance results of quantum ML model experiments involving various adversarial perturbation coefficients (attack strength) values. We argue that our proposed approach is suitable for practical use in realizing a quantum-safe world because, in the current noisy intermediate-scale quantum devices (NISQs) era, quantum noise is complex and challenging to model and therefore complicates the measurement task or benchmarking. The second contribution of our study is the novel hardened hybrid quantum-classical deep learning (DL) model for botnet domain generation algorithm (DGA) detection, employing a model hardening adversarial training technique for mitigating new types of unknown DGA adversaries since new cyberattack approaches from the cyber arms race need to be anticipated. Our analysis shows the vulnerability of the hybrid quantum DL model to adversarial example attacks by as much as a 19% average drop in accuracy. We also found the superior performance of our hardened model obtained average accuracy gains as high as 5.9%. Furthermore, we found that the hybrid quantum-classical DL approach gives the benefit of suppressing the negative impact of quantum noises on the classifier's performance. We demonstrated how to apply our proposed measurement approach in evaluating our novel hybrid quantum DL model and highlighted the adversarial robustness of our model against adversarial example attacks as evidence of the practical implication of our study towards advancing the state of quantum adversarial machine learning research for the quantum-safe world.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.636; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper aims to contribute to the adversarial defense research gap in the current state-of-the-art of adversarial machine learning (ML) attacks and defense. More specifically, it contributes to the metric measurement of the robustness of artificial intelligence (AI)/ML models against adversarial example attacks, which currently remains an open question in the cybersecurity domain and to an even greater extent for quantum computing-based AI/ML applications. We propose a new adversarial robustness measurement approach which measures the statistical properties (such as the average of the accuracies and t-test results) from the performance results of quantum ML model experiments involving various adversarial perturbation coefficients (attack strength) values. We argue that our proposed approach is suitable for practical use in realizing a quantum-safe world because, in the current noisy intermediate-scale quantum devices (NISQs) era, quantum noise is complex and challenging to model and therefore complicates the measurement task or benchmarking. The second contribution of our study is the novel hardened hybrid quantum-classical deep learning (DL) model for botnet domain generation algorithm (DGA) detection, employing a model hardening adversarial training technique for mitigating new types of unknown DGA adversaries since new cyberattack approaches from the cyber arms race need to be anticipated. Our analysis shows the vulnerability of the hybrid quantum DL model to adversarial example attacks by as much as a 19% average drop in accuracy. We also found the superior performance of our hardened model obtained average accuracy gains as high as 5.9%. Furthermore, we found that the hybrid quantum-classical DL approach gives the benefit of suppressing the negative impact of quantum noises on the classifier's performance. We demonstrated how to apply our proposed measurement approach in evaluating our novel hybrid quantum DL model and highlighted the adversarial robustness of our model against adversarial example attacks as evidence of the practical implication of our study towards advancing the state of quantum adversarial machine learning research for the quantum-safe world.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.636; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 9, 発行日 2022-09-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会