یادگیری فدرالی حافظ حریم خصوصی برای شناسایی تهدیدات پیشرفتۀ مانا در سامانۀ اینترنت پهپادها

نوع مقاله : مقاله پژوهشی (توسعه ای)

نویسندگان

1 استادیار، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران.

2 استادیار، دانشکده مهندسی مکانیک، دانشگاه پیام نور، تهران، ایران.

10.48301/kssa.2023.409787.2649

چکیده

اینترنت پهپادها، یک شبکه توزیع شده و غیر متمرکز است که دسترسی پهپادها را به حریم هوایی کنترل شده مرتبط می کند. اتصال پهپادها در این شبکه ها از طریق اینترنت اشیا است. از این رو، این شبکه ها در برابر تمام تهدیدات امنیتی و حریم خصوصی که بر شبکه های اینترنت اشیا اثر می گذارد آسیب پذیر هستند. علاوه بر این، باتوجه به آن که کاربرد این شبکه ها در بسیاری از موارد دارای حساسیت بالایی است، تهدیدات امنیتی بالقوه بیشتری را شامل می شوند. اجزا این شبکه ها با کمک یکدیگر سعی در شناخت تهدیدات پیشرفته و مانا دارند. یکی از روش ها برای شناسایی این تهدیدات، یادگیری ماشین توزیع شده می باشد. در این روش، داده ها برای یک سرور مرکزی ارسال می شود و یادگیری در آن جا انجام می گیرد. ارسال داده ها یا تهدیدات برای سرور مرکزی، حریم خصوصی اجزای شبکه را نقض می نماید. در این صورت، یادگیری فدرالی به شبکه های توزیع شده و غیر متمرکز کمک می کند تا بجای ارسال داده های محلی و سری خود، ماشین یادگیرنده را به صورت محلی آموزش دهند و پارامترهای مدل را با یکدیگر به اشتراک گذارند. از آن جا که پارامترهای مدل های به اشتراک گذاشته نیز ممکن است حاوی اطلاعاتی از تهدیدات زیرشبکه ها باشند، ما در این مقاله یک پروتکل امن و حافظ حریم خصوصی مبتنی بر رمزنگاری همریخت و برای مدل یادگیری فدرالی جهت تشخیص و شناسایی تهدیدات پیشرفته و مانا در شبکه اینترنت پهپادها پیشنهاد می دهیم.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Private Federated Learning for APT Detection in Internet of Drones

نویسندگان [English]

  • Motahareh Dehghan 1
  • Erfan Khosravian 2
1 Assistant Professor, Department of Industrial and System Engineering, Tarbiat Modares University, Tehran, Iran.
2 Assistant Professor, Department of Mechanical Engineering, Payam Noor University, Tehran, Iran.
چکیده [English]

The Internet of Drones (IoD) is a decentralized network that connects drones to controlled airspace. The connection of drones in these networks is through the Internet of Things. Hence, these networks are vulnerable to all the security and privacy threats that affect IoT networks. In addition, as the application of these networks is highly sensitive in many cases, there are greater potential security threats. The components of these networks work together to identify new and advanced threats. One of the ways to identify new and advanced threats in these networks is distributed machine learning where the data is sent to a central server to learn the general model. This model violates the privacy of network components. It also has a very high level of communication. On the other hand, the central server as the only point of failure may have many problems. In this case, federated learning helps distributed and decentralized networks to share their local model instead of sending their local and secret data. Since the shared models may also disclose some information, we propose a secure and privacy-preserving protocol based on homomorphic encryption. The protocol proposed was for federal learning model and detection of new and advanced threats in the Internet of Drones. 

کلیدواژه‌ها [English]

  • Internet of Drones Federated Learning Privacy Homomorphic Encryption Ideal
  • Real Simulation Paradigm

مراجع

[1] Lasi, H., Fettke, P., Kemper, H-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242. https://doi.org/10.1007/s12599-014-0334-4
[2] Gharibi, M., Boutaba, R., & Waslander, S. L. (2016). Internet of Drones. Institute of Electrical and Electronics Engineers Access, 4, 1148-1162. https://doi.org/10.1109/ACCESS. 2016.2537208
[3] Choudhary, G., Sharma, V., Gupta, T., Kim, J., & You, I. (2018, Auguest 29- September 1). Internet of drones (iod): Threats, vulnerability, and security perspectives. The 3rd International Symposium on Mobile Internet Security, Cebu, Philippines. https://doi .org/10.48550/arXiv.1808.00203
[4] Bernama. (2019, June 18). Food delivery via drones in Cyberjaya by end of the month. New Straits Times. https://www.nst.com.my/lifestyle/bots/2019/06/497157/food-deliver y-drones-cyberjaya-end-month
[5] Yazdinejad, A., Parizi, R. M., Dehghantanha, A., Karimipour, H., Srivastava, G., & Aledhari, M. (2021). Enabling Drones in the Internet of Things With Decentralized Blockchain-Based Security. Institute of Electrical and Electronics Engineers Internet of Things Journal, 8(8), 6406-6415. https://doi.org/10.1109/JIOT.2020.3015382
[6] Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18(24), 100187. https://doi.org/10.1016/j .iot.2020.100187
[7] Magistretti, S., & Dell’Era, C. (2019). Unveiling opportunities afforded by emerging technologies: evidences from the drone industry. Technology Analysis & Strategic Management, 31(5), 606-623. https://doi.org/10.1080/09537325.2018.1538497
[8] Paddeu, D., Calvert, T., Clark, B., & Parkhurst, G. (2019). New technology and automation in freight transport and handling systems. Government. https://uwe-repository.work tribe.com/output/851875/new-technology-and-automation-in-freight-transport-and -handling-systems
[9] Vattapparamban, E., İ, G., Yurekli, A. İ., Akkaya, K., & Uluağaç, S. (2016, September 5-9). Drones for smart cities: Issues in cybersecurity, privacy, and public safety. 2016 International Wireless Communications and Mobile Computing Conference, Paphos, Cyprus. https://doi.org/10.1109/IWCMC.2016.7577060
[10] Półka, M., Ptak, S., & Kuziora, Ł. (2017). The Use of UAV's for Search and Rescue Operations. Procedia Engineering, 192, 748-752. https://doi.org/10.1016/j.proeng.2017.06.129
[11] Kharchenko, V., & Torianyk, V. (2018, May 24-27). Cybersecurity of the Internet of Drones: Vulnerabilities analysis and IMECA based assessment. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, Kyiv, UKraine https://doi.org/10.1109/DESSERT.2018.8409160
[12] Riahi Manesh, M., & Kaabouch, N. (2019). Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions. Computers & Security, 85, 386-401. https://doi.org/10.1016/j.cose.2019.05.003
[13] Kovar, D. (2015, March 18). Cybersecurity and non-military UAVs (AKA drones). URSA. https://ursainc.com/2015/03/18/cybersecurity-and-non-military-uavs-aka-drones/
[14] Altawy, R., & Youssef, A. M. (2016). Security, privacy, and safety aspects of civilian drones: A survey. Association for Computing Machinery Transactions on Cyber-Physical Systems, 1(2), 1-25. https://doi.org/10.1145/3001836
[15] Singh, M., Aujla, G. S., Bali, R. S., Vashisht, S., Singh, A., & Jindal, A. (2020, September 25). Blockchain-enabled secure communication for drone delivery: A case study in COVID-like scenarios. The 26th Annual International Conference on Mobile Computing and Networking, London, England, United Kingdom. https://doi.org/10.1145/34140 45.3415937
[16] Garg, N., & Roy, N. (2020, March 3). Acoustic Sensing for Detecting Projectile Attacks on Small Drones. Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, Austin, TX, USA. https://doi.org/10.1145/3376897.3379167
[17] Garg, N., & Roy, N. (2020, March 3). Enabling Self-defense in Small Drones. Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, Austin, TX, USA. https://doi.org/10.1145/3376897.3377866
[18] Srinivas, J., Das, A. K., Kumar, N., & Rodrigues, J. J. P. C. (2019). TCALAS: Temporal Credential-Based Anonymous Lightweight Authentication Scheme for Internet of Drones Environment. IEEE Transactions on Vehicular Technology, 68(7), 6903-6916. https://doi.org/10.1109/TVT.2019.2911672
[19] Ali, Z., Chaudhry, S. A., Ramzan, M. S., & Al-Turjman, F. (2020). Securing Smart City Surveillance: A Lightweight Authentication Mechanism for Unmanned Vehicles. Internet of Drones. Institute of Electrical and Electronics Engineers Access, 8, 43711-43724. https://doi.org/10.1109/ACCESS.2020.2977817
[20] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. https://mi tpress.mit.edu/9780262035613/deep-learning/
[21] Yaacoub, J-P., Noura, H., Salman, O., & Chehab, A. (2020). Security analysis of drones systems: Attacks, limitations, and recommendations. Internet of Things, 11, 100218. https://doi.org/10.1016/j.iot.2020.100218
[22] Pandey, G. K., Gurjar, D. S., Nguyen, H. H., & Yadav, S. (2022). Security Threats and Mitigation Techniques in UAV Communications: A Comprehensive Survey. Institute of Electrical and Electronics Engineers Access, 10, 112858-112897. https://doi.org/ 10.1109/ACCESS.2022.3215975
[23] Kong, P. Y. (2021). A Survey of Cyberattack Countermeasures for Unmanned Aerial Vehicles. Institute of Electrical and Electronics Engineers Access, 9(1), 148244-148263. https ://doi.org/10.1109/ACCESS.2021.3124996
[24] Agyapong, R. A., Nabil, M., Nuhu, A. R., Rasul, M. I., & Homaifar, A. (2021, December 5-7). Efficient Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles Using Deep Learning 2021 IEEE Symposium Series on Computational Intelligence, Orlando, Florida, USA. https://doi.org/10.1109/SSCI50451.2021.9659972
[25] Zhang, D., Chen, X., Wang, D., & Shi, J. (2018, June 18-21). A Survey on Collaborative Deep Learning and Privacy-Preserving. 2018 IEEE Third International Conference on Data Science in Cyberspace, Guangzhou, China https://doi.org/10.1109/DSC.20 18.00104
[26] Westin, A. F. (1968). Privacy And Freedom. Washington and Lee Law Review 25(1), 166-170. https://scholarlycommons.law.wlu.edu/wlulr/vol25/iss1/20/
[27] Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2018). A Survey on Homomorphic Encryption Schemes: Theory and Implementation. Association for Computing Machinery Computing Surveys, 51(4), 1-35. https://doi.org/10.1145/3214303
[28] Lindell, Y., & Pinkas, B. (2009). A Proof of Security of Yao’s Protocol for Two-Party Computation. Journal of Cryptology, 22(2), 161-188. https://doi.org/10.1007/s0014 5-008-9036-8
[29] Dehghan, M., & Sadeghiyan, B. (2019). Privacy-preserving collision detection of moving objects. Transactions on Emerging Telecommunications Technologies, 30(3), e3484. https://doi.org/10.1002/ett.3484
[30] Dehghan, M., Sadeghiyan, B., & Khosravian, E. (2021). Private Trajectory Intersection Testing: Is Garbled Circuit Better than Custom Protocols? International Journal of Engineering, 34(4), 863-872. https://doi.org/10.5829/ije.2021.34.04a.12
[31] Whelan, J., Sangarapillai, T., Minawi, O., Almehmadi, A., & El-Khatib, K. (2020). UAV attack dataset [Data set]. IEEE DataPort. https://ieee-dataport.org/open-access/uav-attack-dataset
[32] Popescu, M-C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. World Scientific and Engineering Academy and Society Transactions on Circuits and Systems, 8(7), 579-588. https://www.researchgate.net/ publication/228340819_Multilayer_perceptron_and_neural_networks
فصلنامه علمی کارافن
دوره 20، شماره 3
فنی و مهندسی
آذر 1402
صفحه 465-484

فایل ها

سابقه مقاله

  • تاریخ دریافت: 13 مرداد 1402
  • تاریخ بازنگری: 29 مرداد 1402
  • تاریخ پذیرش: 20 شهریور 1402

هم رسانی

ارجاع به این مقاله

آمار