Generation of lightweight models for cyber-attacks detection algorithms using knowledge distillation
Апстракт
Industry 4.0 paradigm has brought about the changes in the way we manufacture. The integration of Cyber-Physical Systems into the Industrial Internet of Things represents the basis for the transition from traditionally centralized to distributed control systems where the overall control task is achieved through the cooperation of different devices which implies their mutual communication and constant information exchange. However, ubiquitous communication between devices with communication and computation capabilities opens up space for various cyber-attacks which can lead to catastrophic damage to equipment
and also can endanger the environment and human lives. Therefore, the development and implementation of cyber-attacks detection mechanisms are necessary to prevent negative effects. Deep learning (DL) techniques are successfully applied to generate models on which cyber-attacks detection algorithms are based. However, the size of the DL models is often unsuitable for implementatio...n on industrial control devices that usually have significant computational constraints. The use of complex DL models may disrupt the operation of control systems and introduce unacceptable delays in real-time cyber-attacks detection algorithms. This paper explores the possibilities for application of knowledge distillation technique to generate lightweight DL models. These models are designed to align with the limitations of the devices on which they are deployed. The paper evaluates the performance of lightweight models in cyber-attacks detection algorithms, and compares them to algorithms based on DL models before distillation.
Кључне речи:
Cyber-Physical Systems / Industrial Internet of Things / Cybersecurity, / Cyber-attacks detection / Machine learning / Knowledge distillationИзвор:
39th International conference on production engineering of Serbia (ICPES 2023), 2023, 24-31Финансирање / пројекти:
- Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима (RS-MESTD-Technological Development (TD or TR)-35004)
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Nedeljković, Dušan AU - Jakovljević, Živana PY - 2023 UR - https://machinery.mas.bg.ac.rs/handle/123456789/7009 AB - Industry 4.0 paradigm has brought about the changes in the way we manufacture. The integration of Cyber-Physical Systems into the Industrial Internet of Things represents the basis for the transition from traditionally centralized to distributed control systems where the overall control task is achieved through the cooperation of different devices which implies their mutual communication and constant information exchange. However, ubiquitous communication between devices with communication and computation capabilities opens up space for various cyber-attacks which can lead to catastrophic damage to equipment and also can endanger the environment and human lives. Therefore, the development and implementation of cyber-attacks detection mechanisms are necessary to prevent negative effects. Deep learning (DL) techniques are successfully applied to generate models on which cyber-attacks detection algorithms are based. However, the size of the DL models is often unsuitable for implementation on industrial control devices that usually have significant computational constraints. The use of complex DL models may disrupt the operation of control systems and introduce unacceptable delays in real-time cyber-attacks detection algorithms. This paper explores the possibilities for application of knowledge distillation technique to generate lightweight DL models. These models are designed to align with the limitations of the devices on which they are deployed. The paper evaluates the performance of lightweight models in cyber-attacks detection algorithms, and compares them to algorithms based on DL models before distillation. C3 - 39th International conference on production engineering of Serbia (ICPES 2023) T1 - Generation of lightweight models for cyber-attacks detection algorithms using knowledge distillation EP - 31 SP - 24 UR - https://hdl.handle.net/21.15107/rcub_machinery_7009 ER -
@conference{ author = "Nedeljković, Dušan and Jakovljević, Živana", year = "2023", abstract = "Industry 4.0 paradigm has brought about the changes in the way we manufacture. The integration of Cyber-Physical Systems into the Industrial Internet of Things represents the basis for the transition from traditionally centralized to distributed control systems where the overall control task is achieved through the cooperation of different devices which implies their mutual communication and constant information exchange. However, ubiquitous communication between devices with communication and computation capabilities opens up space for various cyber-attacks which can lead to catastrophic damage to equipment and also can endanger the environment and human lives. Therefore, the development and implementation of cyber-attacks detection mechanisms are necessary to prevent negative effects. Deep learning (DL) techniques are successfully applied to generate models on which cyber-attacks detection algorithms are based. However, the size of the DL models is often unsuitable for implementation on industrial control devices that usually have significant computational constraints. The use of complex DL models may disrupt the operation of control systems and introduce unacceptable delays in real-time cyber-attacks detection algorithms. This paper explores the possibilities for application of knowledge distillation technique to generate lightweight DL models. These models are designed to align with the limitations of the devices on which they are deployed. The paper evaluates the performance of lightweight models in cyber-attacks detection algorithms, and compares them to algorithms based on DL models before distillation.", journal = "39th International conference on production engineering of Serbia (ICPES 2023)", title = "Generation of lightweight models for cyber-attacks detection algorithms using knowledge distillation", pages = "31-24", url = "https://hdl.handle.net/21.15107/rcub_machinery_7009" }
Nedeljković, D.,& Jakovljević, Ž.. (2023). Generation of lightweight models for cyber-attacks detection algorithms using knowledge distillation. in 39th International conference on production engineering of Serbia (ICPES 2023), 24-31. https://hdl.handle.net/21.15107/rcub_machinery_7009
Nedeljković D, Jakovljević Ž. Generation of lightweight models for cyber-attacks detection algorithms using knowledge distillation. in 39th International conference on production engineering of Serbia (ICPES 2023). 2023;:24-31. https://hdl.handle.net/21.15107/rcub_machinery_7009 .
Nedeljković, Dušan, Jakovljević, Živana, "Generation of lightweight models for cyber-attacks detection algorithms using knowledge distillation" in 39th International conference on production engineering of Serbia (ICPES 2023) (2023):24-31, https://hdl.handle.net/21.15107/rcub_machinery_7009 .