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Denetleyici Alan Ağının Güvenliğinin Sağlanması için Derin Öğrenme Tabanlı Saldırı Tespit Sistemleri Üzerine Bir Derleme

Year 2021, Issue: 27, 1038 - 1049, 30.11.2021
https://doi.org/10.31590/ejosat.974582

Abstract

Nesnelerin interneti fikrinin otomotiv alanına girmesi ile birlikte araçların interneti kavramı ortaya çıkmıştır. Araçların interneti hem araç içi ağ iletişimini hem de araçların diğer nesnelerle olan iletişimini kapsamaktadır. Araç içi ağ iletişimi, araç içi çeşitli işlevleri sağlayan Elektronik Kontrol Birimleri arasındaki güvenilir bir iletişimi sağlamakta olup araç içi ağlar arasında en yaygın kullanılanı denetleyici alan ağlarıdır. Denetleyici alan ağı, araç içi ağ için güvenli bir iletişim ortamı sunarken siber saldırılara karşı savunmasızdır. Bu derleme çalışmasında araç içi denetleyici alan ağının güvenliğinin sağlanması için derin öğrenme yöntemini kullanan saldırı tespit sistemleri üzerine odaklanılmıştır. Bu kapsamda veritabanları üzerinde sistematik bir literatür taraması gerçekleştirilerek literatüre yön veren çalışmalar belirlenmiştir. Belirlenen çalışmalar kullanılan yöntem, veri kümesi, seçilen öznitelik ve odaklanılan saldırı bakımından detaylı bir şekilde incelenmiştir. Ayrıca incelenen çalışmalarda önerilen saldırı tespit modelinin performansının nasıl değerlendirildiği ifade edilmekle birlikte önerilen modelin diğer yöntemlerle yapılan karşılaştırmalar detaylandırılmıştır.

References

  • Al-Jarrah, O. Y., Maple, C., Dianati, M., Oxtoby, D., & Mouzakitis, A. (2019). Intrusion detection systems for intra-vehicle networks: A review. IEEE Access, 7, 21266-21289.
  • Aliwa, E., Rana, O., Perera, C., & Burnap, P. (2021). Cyberattacks and countermeasures for in-vehicle networks. ACM Computing Surveys (CSUR), 54(1), 1-37.
  • Amato, F., Coppolino, L., Mercaldo, F., Moscato, F., Nardone, R., & Santone, A. (2021). CAN-Bus Attack Detection With Deep Learning. IEEE Transactions on Intelligent Transportation Systems.
  • Bosch, R. (Ed.). (2014). Bosch automotive electrics and automotive electronics: systems and components, networking and hybrid drive. Springer Vieweg.
  • Bozdal, M., Samie, M., Aslam, S., & Jennions, I. (2020). Evaluation of can bus security challenges. Sensors, 20(8), 2364.
  • Gao, L., Li, F., Xu, X., & Liu, Y. (2019). Intrusion detection system using SOEKS and deep learning for in-vehicle security. Cluster Computing, 22(6), 14721-14729.
  • Han, M. L., Kwak, B. I., & Kim, H. K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular communications, 14, 52-63.
  • Hanselmann, M., Strauss, T., Dormann, K., & Ulmer, H. (2020). CANet: An unsupervised intrusion detection system for high dimensional CAN bus data. IEEE Access, 8, 58194-58205.
  • Hira, E. (2017). Automotive Electronic Control Unit (ECU) Market Size Share, Allied Market Research. Available online: https://www.alliedmarketresearch.com/automotive-electronic-control-unit-ecu-market (accessed on 24 May 2021).
  • Hossain, M. D., Inoue, H., Ochiai, H., Fall, D., & Kadobayashi, Y. (2020). LSTM-based intrusion detection system for in-vehicle can bus communications. IEEE Access, 8, 185489-185502.
  • Hu, Q., & Luo, F. (2018). Review of secure communication approaches for in-vehicle network. International Journal of Automotive Technology, 19(5), 879-894.
  • Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C. T., & Liu, X. (2016). Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects. IEEE Access, 4, 5356-5373.
  • Kang, M. J., & Kang, J. W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PloS one, 11(6), e0155781.
  • Khatri, N., Shrestha, R., & Nam, S. Y. (2021). Security Issues with In-Vehicle Networks, and Enhanced Countermeasures Based on Blockchain. Electronics, 10(8), 893.
  • Lee, H., Jeong, S. H., & Kim, H. K. (2017, August). OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In 2017 15th Annual Conference on Privacy, Security and Trust (PST) (pp. 57-5709). IEEE.
  • Liu, J., Zhang, S., Sun, W., & Shi, Y. (2017). In-vehicle network attacks and countermeasures: Challenges and future directions. IEEE Network, 31(5), 50-58.
  • Lokman, S. F., Othman, A. T., & Abu-Bakar, M. H. (2019). Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1-17.
  • Miller, C. (2019). Lessons learned from hacking a car. IEEE Design & Test, 36(6), 7-9.
  • Pan, L., Zheng, X., Chen, H. X., Luan, T., Bootwala, H., & Batten, L. (2017). Cyber security attacks to modern vehicular systems. Journal of information security and applications, 36, 90-100.
  • Qin, H., Yan, M., & Ji, H. (2021). Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction. Vehicular Communications, 27, 100291.
  • Sharma, N., Chauhan, N., & Chand, N. (2018, December). Security challenges in Internet of Vehicles (IoV) environment. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 203-207). IEEE.
  • Song, H. M., & Kim, H. K. (2021). Self-Supervised Anomaly Detection for In-Vehicle Network Using Noised Pseudo Normal Data. IEEE Transactions on Vehicular Technology, 70(2), 1098-1108.
  • Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198.
  • Sun, J., Iqbal, S., Arabi, N. S., & Zulkernine, M. (2020). A classification of attacks to in-vehicle components (IVCs). Vehicular Communications, 25, 100253.
  • Tariq, S., Lee, S., Kim, H. K., & Woo, S. S. (2020). CAN-ADF: The controller area network attack detection framework. Computers & Security, 94, 101857.
  • Wang, L., & Liu, X. (2018). NOTSA: Novel OBU with three-level security architecture for internet of vehicles. IEEE Internet of Things Journal, 5(5), 3548-3558.
  • Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., & Li, K. (2019). A survey of intrusion detection for in-vehicle networks. IEEE Transactions on Intelligent Transportation Systems, 21(3), 919-933.
  • Yan, S., Malaney, R., Nevat, I., & Peters, G. W. (2014). Optimal information-theoretic wireless location verification. IEEE Transactions on Vehicular Technology, 63(7), 3410-3422.
  • Young, C., Zambreno, J., Olufowobi, H., & Bloom, G. (2019). Survey of automotive controller area network intrusion detection systems. IEEE Design & Test, 36(6), 48-55.
  • Yu, B., Xu, C. Z., & Xiao, B. (2013). Detecting sybil attacks in VANETs. Journal of Parallel and Distributed Computing, 73(6), 746-756.
  • Zhang, J., Li, F., Zhang, H., Li, R., & Li, Y. (2019). Intrusion detection system using deep learning for in-vehicle security. Ad Hoc Networks, 95, 101974.

A Review on Deep Learning Based Intrusion Detection Systems for Ensuring Security of Controller Area Network

Year 2021, Issue: 27, 1038 - 1049, 30.11.2021
https://doi.org/10.31590/ejosat.974582

Abstract

The concept of the Internet of vehicles emerges with the definition of the Internet of things idea into the automotive field. The internet of vehicles covers both in-vehicle network and the communication of vehicles with other things. The in-vehicle network provides reliable communication between Electronic Control Units providing various in-vehicle functions, and the most widely used among in-vehicle networks is the controller area networks. The controller area network is vulnerable to cyber-attacks while providing a secure communication environment for the in-vehicle network. This survey paper focuses on intrusion detection systems that use deep learning to secure the in-vehicle controller area network. In this context, systematic literature research is conducted on scientific/academical databases, and papers are determined that has an effect on the literature. The studies are examined in detail in terms of the used method, dataset, selected attribute, and focused attack. In addition, the previous studies are compared with the others in detail.

References

  • Al-Jarrah, O. Y., Maple, C., Dianati, M., Oxtoby, D., & Mouzakitis, A. (2019). Intrusion detection systems for intra-vehicle networks: A review. IEEE Access, 7, 21266-21289.
  • Aliwa, E., Rana, O., Perera, C., & Burnap, P. (2021). Cyberattacks and countermeasures for in-vehicle networks. ACM Computing Surveys (CSUR), 54(1), 1-37.
  • Amato, F., Coppolino, L., Mercaldo, F., Moscato, F., Nardone, R., & Santone, A. (2021). CAN-Bus Attack Detection With Deep Learning. IEEE Transactions on Intelligent Transportation Systems.
  • Bosch, R. (Ed.). (2014). Bosch automotive electrics and automotive electronics: systems and components, networking and hybrid drive. Springer Vieweg.
  • Bozdal, M., Samie, M., Aslam, S., & Jennions, I. (2020). Evaluation of can bus security challenges. Sensors, 20(8), 2364.
  • Gao, L., Li, F., Xu, X., & Liu, Y. (2019). Intrusion detection system using SOEKS and deep learning for in-vehicle security. Cluster Computing, 22(6), 14721-14729.
  • Han, M. L., Kwak, B. I., & Kim, H. K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular communications, 14, 52-63.
  • Hanselmann, M., Strauss, T., Dormann, K., & Ulmer, H. (2020). CANet: An unsupervised intrusion detection system for high dimensional CAN bus data. IEEE Access, 8, 58194-58205.
  • Hira, E. (2017). Automotive Electronic Control Unit (ECU) Market Size Share, Allied Market Research. Available online: https://www.alliedmarketresearch.com/automotive-electronic-control-unit-ecu-market (accessed on 24 May 2021).
  • Hossain, M. D., Inoue, H., Ochiai, H., Fall, D., & Kadobayashi, Y. (2020). LSTM-based intrusion detection system for in-vehicle can bus communications. IEEE Access, 8, 185489-185502.
  • Hu, Q., & Luo, F. (2018). Review of secure communication approaches for in-vehicle network. International Journal of Automotive Technology, 19(5), 879-894.
  • Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C. T., & Liu, X. (2016). Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects. IEEE Access, 4, 5356-5373.
  • Kang, M. J., & Kang, J. W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PloS one, 11(6), e0155781.
  • Khatri, N., Shrestha, R., & Nam, S. Y. (2021). Security Issues with In-Vehicle Networks, and Enhanced Countermeasures Based on Blockchain. Electronics, 10(8), 893.
  • Lee, H., Jeong, S. H., & Kim, H. K. (2017, August). OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In 2017 15th Annual Conference on Privacy, Security and Trust (PST) (pp. 57-5709). IEEE.
  • Liu, J., Zhang, S., Sun, W., & Shi, Y. (2017). In-vehicle network attacks and countermeasures: Challenges and future directions. IEEE Network, 31(5), 50-58.
  • Lokman, S. F., Othman, A. T., & Abu-Bakar, M. H. (2019). Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1-17.
  • Miller, C. (2019). Lessons learned from hacking a car. IEEE Design & Test, 36(6), 7-9.
  • Pan, L., Zheng, X., Chen, H. X., Luan, T., Bootwala, H., & Batten, L. (2017). Cyber security attacks to modern vehicular systems. Journal of information security and applications, 36, 90-100.
  • Qin, H., Yan, M., & Ji, H. (2021). Application of Controller Area Network (CAN) bus anomaly detection based on time series prediction. Vehicular Communications, 27, 100291.
  • Sharma, N., Chauhan, N., & Chand, N. (2018, December). Security challenges in Internet of Vehicles (IoV) environment. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 203-207). IEEE.
  • Song, H. M., & Kim, H. K. (2021). Self-Supervised Anomaly Detection for In-Vehicle Network Using Noised Pseudo Normal Data. IEEE Transactions on Vehicular Technology, 70(2), 1098-1108.
  • Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198.
  • Sun, J., Iqbal, S., Arabi, N. S., & Zulkernine, M. (2020). A classification of attacks to in-vehicle components (IVCs). Vehicular Communications, 25, 100253.
  • Tariq, S., Lee, S., Kim, H. K., & Woo, S. S. (2020). CAN-ADF: The controller area network attack detection framework. Computers & Security, 94, 101857.
  • Wang, L., & Liu, X. (2018). NOTSA: Novel OBU with three-level security architecture for internet of vehicles. IEEE Internet of Things Journal, 5(5), 3548-3558.
  • Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., & Li, K. (2019). A survey of intrusion detection for in-vehicle networks. IEEE Transactions on Intelligent Transportation Systems, 21(3), 919-933.
  • Yan, S., Malaney, R., Nevat, I., & Peters, G. W. (2014). Optimal information-theoretic wireless location verification. IEEE Transactions on Vehicular Technology, 63(7), 3410-3422.
  • Young, C., Zambreno, J., Olufowobi, H., & Bloom, G. (2019). Survey of automotive controller area network intrusion detection systems. IEEE Design & Test, 36(6), 48-55.
  • Yu, B., Xu, C. Z., & Xiao, B. (2013). Detecting sybil attacks in VANETs. Journal of Parallel and Distributed Computing, 73(6), 746-756.
  • Zhang, J., Li, F., Zhang, H., Li, R., & Li, Y. (2019). Intrusion detection system using deep learning for in-vehicle security. Ad Hoc Networks, 95, 101974.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Zinnet Duygu Akşehir 0000-0002-6834-6847

Sedat Akleylek 0000-0001-7005-6489

Early Pub Date July 29, 2021
Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 27

Cite

APA Akşehir, Z. D., & Akleylek, S. (2021). Denetleyici Alan Ağının Güvenliğinin Sağlanması için Derin Öğrenme Tabanlı Saldırı Tespit Sistemleri Üzerine Bir Derleme. Avrupa Bilim Ve Teknoloji Dergisi(27), 1038-1049. https://doi.org/10.31590/ejosat.974582