Cyber-attack detection method based on RNN
Апстракт
Current and forthcoming market requirements bring huge challenges to today manufacturing. Answer to the changing demands and high product variety is found in the integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into industrial plants. CPS as smart devices capable of data processing and information exchange enable fast adaptation of manufacturing resources to production of diversified products. Nevertheless, fully implemented internet communication at factory shop floor opens up a whole new area for potential cyber-attacks. The consequences of attacks can have a negative influence on the system or even endanger human lives. Therefore, defence techniques must be developed to ensure a high level of protection. Early detection of cyber-attacks is crucial to minimize or completely avoid the negative effects of the attack and keep the system safe and reliable. In this work, we propose an attack detection method based on deep learning approach. We explore the appli...cation of several deep learning architectures based on Simple Recurrent Neural Networks (Simple RNN) and Long Short-Term Memory (LSTM) based
RNN for generation of the detection mechanisms tailored to the concrete process. Our method was experimentally verified using
real world data and it proved to be effective, as it detected all considered attacks without false positives.
Кључне речи:
Cyber security / Cyber Physical Systems / Internet of Things / Deep learning / Recurrent Neural NetworkИзвор:
7th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2020), Proceedings, Belgrade, Čačak, Niš, Novi Sad, September 2020., 2020, 726-731Издавач:
- ETRAN Society, Belgrade, Academic Mind, Belgrade
Финансирање / пројекти:
- MISSION4.0 - Deep Machine Learning and Swarm Intelligence-Based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0 (RS-ScienceFundRS-AI-6523109)
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Nedeljković, Dušan AU - Jakovljević, Živana PY - 2020 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5270 AB - Current and forthcoming market requirements bring huge challenges to today manufacturing. Answer to the changing demands and high product variety is found in the integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into industrial plants. CPS as smart devices capable of data processing and information exchange enable fast adaptation of manufacturing resources to production of diversified products. Nevertheless, fully implemented internet communication at factory shop floor opens up a whole new area for potential cyber-attacks. The consequences of attacks can have a negative influence on the system or even endanger human lives. Therefore, defence techniques must be developed to ensure a high level of protection. Early detection of cyber-attacks is crucial to minimize or completely avoid the negative effects of the attack and keep the system safe and reliable. In this work, we propose an attack detection method based on deep learning approach. We explore the application of several deep learning architectures based on Simple Recurrent Neural Networks (Simple RNN) and Long Short-Term Memory (LSTM) based RNN for generation of the detection mechanisms tailored to the concrete process. Our method was experimentally verified using real world data and it proved to be effective, as it detected all considered attacks without false positives. PB - ETRAN Society, Belgrade, Academic Mind, Belgrade C3 - 7th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2020), Proceedings, Belgrade, Čačak, Niš, Novi Sad, September 2020. T1 - Cyber-attack detection method based on RNN EP - 731 SP - 726 UR - https://hdl.handle.net/21.15107/rcub_machinery_5270 ER -
@conference{ author = "Nedeljković, Dušan and Jakovljević, Živana", year = "2020", abstract = "Current and forthcoming market requirements bring huge challenges to today manufacturing. Answer to the changing demands and high product variety is found in the integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into industrial plants. CPS as smart devices capable of data processing and information exchange enable fast adaptation of manufacturing resources to production of diversified products. Nevertheless, fully implemented internet communication at factory shop floor opens up a whole new area for potential cyber-attacks. The consequences of attacks can have a negative influence on the system or even endanger human lives. Therefore, defence techniques must be developed to ensure a high level of protection. Early detection of cyber-attacks is crucial to minimize or completely avoid the negative effects of the attack and keep the system safe and reliable. In this work, we propose an attack detection method based on deep learning approach. We explore the application of several deep learning architectures based on Simple Recurrent Neural Networks (Simple RNN) and Long Short-Term Memory (LSTM) based RNN for generation of the detection mechanisms tailored to the concrete process. Our method was experimentally verified using real world data and it proved to be effective, as it detected all considered attacks without false positives.", publisher = "ETRAN Society, Belgrade, Academic Mind, Belgrade", journal = "7th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2020), Proceedings, Belgrade, Čačak, Niš, Novi Sad, September 2020.", title = "Cyber-attack detection method based on RNN", pages = "731-726", url = "https://hdl.handle.net/21.15107/rcub_machinery_5270" }
Nedeljković, D.,& Jakovljević, Ž.. (2020). Cyber-attack detection method based on RNN. in 7th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2020), Proceedings, Belgrade, Čačak, Niš, Novi Sad, September 2020. ETRAN Society, Belgrade, Academic Mind, Belgrade., 726-731. https://hdl.handle.net/21.15107/rcub_machinery_5270
Nedeljković D, Jakovljević Ž. Cyber-attack detection method based on RNN. in 7th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2020), Proceedings, Belgrade, Čačak, Niš, Novi Sad, September 2020.. 2020;:726-731. https://hdl.handle.net/21.15107/rcub_machinery_5270 .
Nedeljković, Dušan, Jakovljević, Živana, "Cyber-attack detection method based on RNN" in 7th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2020), Proceedings, Belgrade, Čačak, Niš, Novi Sad, September 2020. (2020):726-731, https://hdl.handle.net/21.15107/rcub_machinery_5270 .