{"type":"ScholarlyArticle","title":"AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes","authors":[{"name":"Vitaliy Pozdnyakov","slug":"vitaliy-pozdnyakov","given":"Vitaliy","family":"Pozdnyakov","affiliation":"AIRI , Moscow , Russia; ISP RAS Research Center for Trusted Artificial Intelligence"},{"name":"Aleksandr Kovalenko","slug":"aleksandr-kovalenko","given":"Aleksandr","family":"Kovalenko","affiliation":"AIRI , Moscow , Russia; ISP RAS Research Center for Trusted Artificial Intelligence"},{"name":"Ilya Makarov","slug":"ilya-makarov","given":"Ilya","family":"Makarov","affiliation":"AIRI , Moscow , Russia; ISP RAS Research Center for Trusted Artificial Intelligence"},{"name":"Mikhail Drobyshevskiy","slug":"mikhail-drobyshevskiy","given":"Mikhail","family":"Drobyshevskiy","affiliation":"Ivannikov Institute for System Programming of the Russian Academy of Sciences; Moscow Institute of Physics and Technology (National Research University); ISP RAS Research Center for Trusted Artificial Intelligence"},{"name":"Kirill Lukyanov","slug":"kirill-lukyanov","given":"Kirill","family":"Lukyanov","affiliation":"Ivannikov Institute for System Programming of the Russian Academy of Sciences; Moscow Institute of Physics and Technology (National Research University); ISP RAS Research Center for Trusted Artificial Intelligence"}],"abstract":"The development of the smart manufacturing trend includes the integration of Artificial Intelligence technologies into industrial processes. One example of such implementation is deep learning models that diagnose the current state of a technological process. Recent studies have demonstrated that small data perturbations, named adversarial attacks, can significantly affect the correct predictions of such models. This fact is critical in industrial systems, where AI-based decisions can be made to manage physical equipment. In this work, we present a system which can help to evaluate the robustness of technological process diagnosis models to adversarial attacks, as well as consider protection options. We briefly review the system's modules and also consider some useful applications. Our demo video is available at: http://tinyurl.com/3by9zcj5","doi":"10.24963/ijcai.2024/1030","url":"https://ijcai.org/proceedings/2024/1030","pdf":"https://ijcai.org/proceedings/2024/1030.pdf","markdown":"https://ijcai.org/proceedings/2024/1030.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1030","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1030,"pages":{"first":8776,"last":8779},"keywords":["Multidisciplinary Topics and Applications: MDA: Real-time systems","Multidisciplinary Topics and Applications: MDA: Security and privacy","Machine Learning: ML: Adversarial machine learning","Machine Learning: ML: Evaluation"],"videos":[],"references":[{"position":1,"raw":"[Carlini and Wagner, 2017] Nicholas Carlini and David Wagner. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp), pages 39-57. Ieee, 2017.","title":"Towards evaluating the robustness of neural networks","authors":"Wagner Carlini, Nicholas Carlini, David Wagner","year":"2017","venue":"ieee symposium on security and privacy (sp)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cui et al., 2020] Weiyu Cui, Xiaorui Li, Jiawei Huang, Wenyi Wang, Shuai Wang, and Jianwen Chen. Substitute model generation for black-box adversarial attack based on knowledge distillation. In 2020 IEEE International Conference on Image Processing (ICIP), pages 648-652. IEEE, 2020.","title":"Substitute model generation for black-box adversarial attack based on knowledge distillation","authors":"Weiyu Cui, Xiaorui Li, Jiawei Huang, Wenyi Wang, Shuai Wang, Jianwen Chen","year":"2020","venue":"IEEE International Conference on Image Processing (ICIP)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Finlay and Oberman, 2021] Chris Finlay and Adam M Oberman. Scaleable input gradient regularization for ad- versarial robustness. Machine Learning with Applications, 3:100017, 2021.","title":"Scaleable input gradient regularization for adversarial robustness","authors":"Chris Finlay, Adam M Oberman","year":"2021","venue":"Machine Learning with Applications","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ganeeva et al., 2024] Veronika Ganeeva, Kuzma Khrabrov, Artur Kadurin, Andrey Savchenko, and Elena Tutubalina. Chemical language models have problems with chemistry: A case study on molecule captioning task. In The Second Tiny Papers Track at ICLR 2024, 2024.","title":"Chemical language models have problems with chemistry: A case study on molecule captioning task","authors":"Veronika Ganeeva, Kuzma Khrabrov, Artur Kadurin, Andrey Savchenko, Elena Tutubalina","year":"2024","venue":"The Second Tiny Papers Track at ICLR 2024","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Golyadkin et al., 2023] Maksim Golyadkin, Vitaliy Pozd- nyakov, Leonid Zhukov, and Ilya Makarov. Sensorscan: Self-supervised learning and deep clustering for fault di- agnosis in chemical processes. Artificial Intelligence, 324:104012, 2023.","title":"Sensorscan: Self-supervised learning and deep clustering for fault diagnosis in chemical processes","authors":"Maksim Golyadkin, Vitaliy Pozdnyakov, Leonid Zhukov, Ilya Makarov","year":"2023","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Goodfellow et al., 2014] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.","title":"Explaining and harnessing adversarial examples","authors":"Ian J Goodfellow, Jonathon Shlens, Christian Szegedy","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Harris et al., 2020] Charles R Harris, K Jarrod Millman, Stéfan J Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al. Array programming with numpy. Nature, 585(7825):357-362, 2020.","title":"Array programming with numpy","authors":"K Charles R Harris, Jarrod Millman, J Stéfan, Ralf Van Der Walt, Pauli Gommers, David Virtanen, Eric Cournapeau, Julian Wieser, Sebastian Taylor, Nathaniel J Berg, Smith","year":"2020","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jolly, 2018] Kevin Jolly. Hands-on data visualization with Bokeh: Interactive web plotting for Python using Bokeh. Packt Publishing Ltd, 2018.","title":"Hands-on data visualization with Bokeh: Interactive web plotting for Python using Bokeh","authors":"Kevin Jolly","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Khoualdia et al., 2021] Tarek Khoualdia, Abdelaziz Lake- hal, Zoubir Chelli, Kais Khoualdia, and Karim Nessaib. Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals. Diagnostyka, 22, 2021.","title":"Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals","authors":"Tarek Khoualdia, Abdelaziz Lakehal, Zoubir Chelli, Kais Khoualdia, Karim Nessaib","year":"2021","venue":"Diagnostyka","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lessmeier et al., 2016] Christian Lessmeier, James Kuria Kimotho, Detmar Zimmer, and Walter Sextro. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In PHM Society European Conference, volume 3, 2016.","title":"Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification","authors":"Lessmeier","year":"2016","venue":"PHM Society European Conference","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lomov et al., 2021] Ildar Lomov, Mark Lyubimov, Ilya Makarov, and Leonid E Zhukov. Fault detection in tennessee eastman process with temporal deep learning models. Journal of Industrial Information Integration, 23:100216, 2021.","title":"Fault detection in tennessee eastman process with temporal deep learning models","authors":"Ildar Lomov, Mark Lyubimov, Ilya Makarov, Leonid E Zhukov","year":"2021","venue":"Journal of Industrial Information Integration","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Madry et al., 2017] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083, 2017.","title":"Towards deep learning models resistant to adversarial attacks","authors":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Moosavi-Dezfooli et al., 2016] Seyed-Mohsen Moosavi- Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deep- fool: a simple and accurate method to fool deep neural networks. In Proceedings of the IEEE conference on com- puter vision and pattern recognition, pages 2574-2582, 2016.","title":"Deepfool: a simple and accurate method to fool deep neural networks","authors":"Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Pandya et al., 2014] D H Pandya, Sanjay H Upadhyay, and Suraj Prakash Harsha. Fault diagnosis of rolling ele- ment bearing by using multinomial logistic regression and wavelet packet transform. Soft Computing, 18:255-266, 2014.","title":"Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform","authors":"D H Pandya, Sanjay H Upadhyay, Suraj Prakash, Harsha","year":"2014","venue":"Soft Computing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Papernot et al., 2016] Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. Distillation as a defense to adversarial perturbations against deep neu- ral networks. In 2016 IEEE symposium on security and privacy (SP), pages 582-597. IEEE, 2016.","title":"Distillation as a defense to adversarial perturbations against deep neural networks","authors":"Nicolas Papernot, Patrick Mcdaniel, Xi Wu, Somesh Jha, Ananthram Swami","year":"2016","venue":"IEEE symposium on security and privacy (SP)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Paszke et al., 2017] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary De- Vito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.","title":"Automatic differentiation in pytorch","authors":"Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary De-Vito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pozdnyakov et al., 2024] Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, and Kirill Lukyanov. Adversarial attacks and defenses in au- tomated control systems: A comprehensive benchmark. arXiv preprint arXiv:2403.13502, 2024.","title":"Adversarial attacks and defenses in automated control systems: A comprehensive benchmark","authors":"Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Prokhorenkova et al., 2018] Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. Catboost: unbiased boosting with categor- ical features. Advances in neural information processing systems, 31, 2018.","title":"Catboost: unbiased boosting with categorical features","authors":"Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Reinartz et al., 2021] Christopher Reinartz, Murat Kulahci, and Ole Ravn. An extended tennessee eastman simulation dataset for fault-detection and decision support systems. Computers & Chemical Engineering, 149:107281, 2021.","title":"An extended tennessee eastman simulation dataset for fault-detection and decision support systems","authors":"Christopher Reinartz, Murat Kulahci, Ole Ravn","year":"2021","venue":"Computers & Chemical Engineering","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Rieth et al., 2017] Cory A Rieth, Ben D Amsel, Randy Tran, and Maia B Cook. Additional tennessee eastman process simulation data for anomaly detection evaluation. Harvard Dataverse, 1:2017, 2017.","title":"Additional tennessee eastman process simulation data for anomaly detection evaluation","authors":"Cory A Rieth, Ben D Amsel, Randy Tran, Maia B Cook","year":"2017","venue":"Harvard Dataverse","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Xu et al., 2017] Weilin Xu, David Evans, and Yanjun Qi. Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155, 2017.","title":"Feature squeezing: Detecting adversarial examples in deep neural networks","authors":"Weilin Xu, David Evans, Yanjun Qi","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhuo et al., 2022] Yue Zhuo, Zhenqin Yin, and Zhiqiang Ge. Attack and defense: Adversarial security of data- driven fdc systems. IEEE Transactions on Industrial In- formatics, 19(1):5-19, 2022.","title":"Attack and defense: Adversarial security of datadriven fdc systems","authors":"Yue Zhuo, Zhenqin Yin, Zhiqiang Ge","year":"2022","venue":"IEEE Transactions on Industrial Informatics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}