Mạng lưới thông minh trong môi trường đối kháng: thách thức và cơ hội

Springer Science and Business Media LLC - Tập 65 - Trang 1-11 - 2022
Yi Zhao1, Ke Xu1,2, Qi Li3,2, Haiyang Wang4, Dan Wang5, Min Zhu1
1Department of Computer Science and Technology, Tsinghua University, Beijing, China
2Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
3Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China
4Department of Computer Science, University of Minnesota Duluth, Duluth, USA
5Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

Tóm tắt

Mặc dù các công nghệ học sâu đã được khai thác rộng rãi trong nhiều lĩnh vực, nhưng chúng rất dễ bị tấn công đối kháng bằng cách thêm những biến đổi nhỏ vào các đầu vào hợp lệ để đánh lừa các mô hình mục tiêu. Tuy nhiên, rất ít nghiên cứu tập trung vào mạng lưới thông minh trong môi trường đối kháng như vậy, điều này có thể dẫn đến những nguy cơ bảo mật nghiêm trọng. Thực tế, trong khi thách thức mạng lưới thông minh, môi trường đối kháng cũng mang lại những cơ hội. Trong bài báo này, chúng tôi, lần đầu tiên, phân tích đồng thời những thách thức và cơ hội mà môi trường đối kháng mang lại cho mạng lưới thông minh. Cụ thể, chúng tôi tập trung vào những thách thức mà môi trường đối kháng sẽ đặt ra đối với mạng lưới thông minh hiện có. Hơn nữa, chúng tôi điều tra các khung và phương pháp kết hợp học máy đối kháng với mạng lưới thông minh để giải quyết những thiếu sót hiện có của mạng lưới thông minh. Cuối cùng, chúng tôi tóm tắt các vấn đề, bao gồm cả cơ hội và thách thức, cho phép các nhà nghiên cứu tập trung vào mạng lưới thông minh trong các môi trường đối kháng.

Từ khóa

#mạng lưới thông minh #môi trường đối kháng #học sâu #tấn công đối kháng #mã hóa bảo mật

Tài liệu tham khảo

Cabaj K, Mazurczyk W, Nowakowski P, et al. Towards distributed network covert channels detection using data mining-based approach. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, 2018. 12 Mirsky Y, Doitshman T, Elovici Y, et al. KitSune: an ensemble of autoencoders for online network intrusion detection. In: Proceedings of Network and Distributed Systems Security Symposium, 2018 Chen L, Lingys J, Chen K, et al. AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization. In: Proceedings of ACM SIGCOMM, 2018. 191–205 Bega D, Gramaglia M, Fiore M, et al. AZTEC: anticipatory capacity allocation for zero-touch network slicing. In: Proceedings of IEEE INFOCOM, 2020. 794–803 Zhao Y, Qiao M N, Wang H Y, et al. TDFI: two-stage deep learning framework for friendship inference via multi-source information. In: Proceedings of IEEE INFOCOM, 2019. 1981–1989 Benzaid C, Taleb T. AI-driven zero touch network and service management in 5G and beyond: challenges and research directions. IEEE Network, 2020, 34: 186–194 Lei K, Liang Y Z, Li W. Congestion control in SDN-based networks via multi-task deep reinforcement learning. IEEE Network, 2020, 34: 28–34 Gong S M, Lu X, Hoang D T, et al. Toward smart wireless communications via intelligent reflecting surfaces: a contemporary survey. IEEE Commun Surv Tut, 2020, 22: 2283–2314 Lin Y, Zhao H J, Tu Y, et al. Threats of adversarial attacks in DNN-based modulation recognition. In: Proceedings of IEEE INFOCOM, 2020. 2469–2478 Sagduyu Y E, Shi Y, Erpek T. Adversarial deep learning for over-the-air spectrum poisoning attacks. IEEE Trans Mobile Comput, 2021, 20: 306–319 Qiu H, Dong T, Zhang T W, et al. Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet Things J, 2021, 8: 10327–10335 Han D Q, Wang Z L, Zhong Y, et al. Evaluating and improving adversarial robustness of machine learning-based network intrusion detectors. IEEE J Sel Areas Commun, 2021, 39: 2632–2647 Xu Z Y, Tang J, Yin C X, et al. Experience-driven congestion control: when multi-path TCP meets deep reinforcement learning. IEEE J Sel Areas Commun, 2019, 37: 1325–1336 Boyan J A, Littman M L. Packet routing in dynamically changing networks: a reinforcement learning approach. In: Proceedings of Conference and Workshop on Neural Information Processing Systems, 1994. 671–678 Fu C P, Li Q, Shen M, et al. Realtime robust malicious traffic detection via frequency domain analysis. In: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2021. 3431–3446 Liu C Y, Xu M W, Yang Y, et al. DRL-OR: deep reinforcement learning-based online routing for multi-type service requirements. In: Proceedings of IEEE INFOCOM, 2021 Yan S Y, Wang X L, Zheng X L, et al. ACC: automatic ECN tuning for high-speed datacenter networks. In: Proceedings of ACM SIGCOMM, 2021. 384–397 Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Proceedings of Conference and Workshop on Neural Information Processing Systems, 2014. 2672–2680 Ma X J, Li B, Wang Y S, et al. Characterizing adversarial subspaces using local intrinsic dimensionality. In: Proceedings of International Conference on Learning Representations, 2018 Li J, Liu Y, Chen T, et al. Adversarial attacks and defenses on cyber-physical systems: a survey. IEEE Internet Things J, 2020, 7: 5103–5115 Wang N, Chen Y M, Hu Y, et al. MANDA: on adversarial example detection for network intrusion detection system. IEEE Trans Depend Secure Comput, 2022. doi: https://doi.org/10.1109/TDSC.2022.3148990 Treu M, Le T N, Nguyen H H, et al. Fashion-guided adversarial attack on person segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2021. 943–952 Chen Y X, Yuan X J, Zhang J S, et al. Devil’s whisper: a general approach for physical adversarial attacks against commercial black-box speech recognition devices. In: Proceedings of USENIX Security, 2020. 2667–2684 Wu F, Long Y H, Zhang C, et al. LinkTeller: recovering private edges from graph neural networks via influence analysis. In: Proceedings of IEEE Symposium on Security and Privacy (SP), 2022 Xie C L, Chen M H, Chen P Y, et al. CRFL: certifiably robust federated learning against backdoor attacks. In: Proceedings of International Conference on Machine Learning, 2021. 11372–11382 Yatsura M, Metzen J, Hein M. Meta-learning the search distribution of black-box random search based adversarial attacks. In: Proceedings of Conference and Workshop on Neural Information Processing Systems, 2021 Chivukula A S, Liu W. Adversarial deep learning models with multiple adversaries. IEEE Trans Knowl Data Eng, 2019, 31: 1066–1079 Zhao Y, Xu K, Wang H Y, et al. Stability-based analysis and defense against backdoor attacks on edge computing services. IEEE Network, 2021, 35: 163–169 Hameed M Z, Gyorgy A, Gunduz D. The best defense is a good offense: adversarial attacks to avoid modulation detection. IEEE Trans Inform Forensic Secur, 2020, 16: 1074–1087 Usama M, Mitra R, Ilahi I, et al. Examining machine learning for 5G and beyond through an adversarial lens. IEEE Internet Comput, 2021, 25: 26–34 Zanella-Beguelin S, Tople S, Paverd A, et al. Grey-box extraction of natural language models. In: Proceedings of International Conference on Machine Learning, 2021. 12278–12286 Goodfellow I J, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. In: Proceedings of International Conference on Learning Representations, 2015 Madry A, Makelov A, Schmidt L, et al. Towards deep learning models resistant to adversarial attacks. In: Proceedings of International Conference on Learning Representations, 2018 Moosavi-Dezfooli S M, Fawzi A, Frossard P. Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016. 2574–2582 Carlini N, Wagner D. Towards evaluating the robustness of neural networks. In: Proceedings of IEEE Symposium on Security and Privacy (SP), 2017. 39–57 Yang Z L, Li B, Chen P Y, et al. Characterizing audio adversarial examples using temporal dependency. In: Proceedings of International Conference on Learning Representations, 2019 Wang X F, Han Y W, Wang C Y, et al. In-Edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network, 2019, 33: 156–165 Yin C L, Zhu Y F, Fei J L, et al. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 2017, 5: 21954–21961 Diro A, Chilamkurti N. Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun Mag, 2018, 56: 124–130 Liang E, Zhu H, Jin X, et al. Neural packet classification. In: Proceedings of ACM SIGCOMM, 2019. 256–269 Lin Y C, Hong Z W, Liao Y H, et al. Tactics of adversarial attack on deep reinforcement learning agents. In: Proceedings of International Joint Conference on Artificial Intelligence, 2017. 3756–3762 Wang F, Zhong C, Gursoy M C, et al. Defense strategies against adversarial jamming attacks via deep reinforcement learning. In: Proceedings of the 54th Annual Conference on Information Sciences and Systems (CISS), 2020. 1–6 Qu Y Y, Zhang J W, Li R D, et al. Generative adversarial networks enhanced location privacy in 5G networks. Sci China Inf Sci, 2020, 63: 220303 Liu Y, Zhao Y, Zhou G M, et al. FedPrune: personalized and communication-efficient federated learning on non-IID data. In: Proceedings of International Conference on Neural Information Processing, 2021. 430–437 Hitaj B, Ateniese G, Perez-Cruz F. Deep models under the GAN: information leakage from collaborative deep learning. In: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2017. 603–618