A fine-grained intrusion protection system for inter-edge trust transfer

Abstract

The phenomenal popularity of smart mobile computing hardware is enabling pervasive edge intelligence and ushering us into a digital twin era. However, the natural barrier between edge equipment owned by different interested parties poses unique challenges for cross-domain trust management. In addition, the openness of radio access and the accessibility of edge services render edge intelligence systems vulnerable and put sensitive user data in jeopardy. This paper presents an intrusion protection mechanism for edge trust transfer to address the inter-edge trust management issue and the conundrum of detecting indistinguishable malevolent nodes launching weak attacks. First, an inter-edge reputation transfer framework is established to leverage the trust quality of different edges to retain the accumulated trust histories of users when they roam in multi-edge environments structurally. Second, a fine-grained intrusion protection system is proposed to reduce the negative impact of attacks on user interactions and improve the overall trust quality and system security of edge intelligence services. The experimental results validate the effectiveness and superior performance of the proposed intrusion protection for edge trust transfer in securing, enhancing, and consolidating edge intelligence services.

Keywords

Edge computing / Cross-domain trust management / Trust transfer / Intrusion protection

References

[1] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, A.C. Bovik, UGC-VQA: benchmarking blind video quality assessment for user generated content, IEEE Trans. Image Process. 30 (1) (2021) 4449-4464.

[2] Thomson Reuters, China's Bilibili launches paywall as it seeks new revenue source. https://www.usnews.com/news/technology/articles/2022-06-21/chinas-bilibili-launches-paywall-as-it-seeks-new-revenue-source, 2022. (Accessed 21 June 2022).

[3] Statista,Average number of monthly active users of Bilibili Inc, from 1st quarter 2018 to 4th quarter 2021, https://www.statista.com/statistics/1109108/bilibili-average-monthly-active-users/, 2022. (Accessed 20 April 2022).

[4] B. Yang, D. Wu, R. Wang, CUE: an intelligent edge computing framework, IEEE Netw. 33 (3) (2019) 18-25.

[5] L. Xiao, Y. Ding, D. Jiang, J. Huang, D. Wang, J. Li, H.V. Poor, A reinforcement learning and blockchain-based trust mechanism for edge networks, IEEE Trans. Commun. 68 (9) (2020) 5460-5470.

[6] L. Wei, Y. Yang, J. Wu, C. Long, B. Li, Trust management for Internet of Things: a comprehensive study, IEEE Internet Things J. 9 (10) (2022) 7664-7679.

[7] K.A. Awan, I.U. Din, M. Zareei, M. Talha, M. Guizani, S.U. Jadoon, HoliTrust-a holistic cross-domain trust management mechanism for service-centric Internet of Things, IEEE Access 7 (1) (2019) 52191-52201.

[8] R. Chen, et al. BIdM: a blockchain-enabled cross-domain identity management system, J. Commun., Netw. 6 (1) (2021) 44-58.

[9] M. Wang, L. Rui, Y. Yang, Z. Gao, X. Chen, A blockchain-based multi-CA cross-domain authentication scheme in decentralized autonomous network, IEEE Trans. Netw. Serv. Manag. 19 (3) (2022) 2664-2676.

[10] K. Fan, Y. Bai, H. Xu, Q. Pan, H. Li, Y. Yang, A secure cross-domain access control scheme in social networks, in: International Conference on Communications (ICC), IEEE, 2019, pp. 1-6.

[11] A. Paranjothi, M. Atiquzzaman, A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing, Digit. Commun. Netw. 8 (5) (2022) 814-824.

[12] F. S. Al-Anzi, Design and analysis of intrusion detection systems for wireless mesh networks, Digit. Commun. Netw. (early access 2022).

[13] A. Kumar, K. Abhishek, M.R. Ghalib, A. Shankar, X. Cheng, Intrusion detection and prevention system for an IoT environment, Digit. Commun. Netw. 8 (4) (2022) 540-551.

[14] B. Weinger, J. Kim, A. Sim, M. Nakashima, N. Moustafa, K.J. Wu, Enhancing IoT anomaly detection performance for federated learning, Digit. Commun. Netw. 8 (3)(2022) 314-323.

[15] V.B. Reddy, A. Negi, S. Venkataraman, V.R. Venkataraman, A similarity based trust model to mitigate badmouthing attacks in Internet of Things (IoT), in: 5th World Forum on Internet of Things (WF-IoT), IEEE, 2019, pp. 278-282.

[16] Z. Yang, Q. Sun, Z. Liu, Three birds with one stone: user intention understanding and influential neighbor disclosure for injection attack detection, IEEE Trans. Inf. Forensics Secur. 17 (1) (2022) 531-546.

[17] E. Anthi, L. Williams, M. Słowińska, G. Theodorakopoulos, P. Burnap, A supervised intrusion detection system for smart home IoT devices, IEEE Internet Things J. 6 (5)(2019) 9042-9053.

[18] J. Kang, et al., Blockchain for secure and efficient data sharing in vehicular edge computing and networks, IEEE Internet Things J. 6 (3) (2019) 4660-4670.

[19] J. Wang, L. Wu, K.K.R. Choo, D. He, Blockchain-based anonymous authentication with key management for smart grid edge computing infrastructure, IEEE Trans. Ind. Inf. 16 (3) (2020) 1984-1992.

[20] A. Yazdinejad, R.M. Parizi, A. Dehghantanha, Q. Zhang, K.-K.R. Choo, An energy-efficient SDN controller architecture for IoT networks with blockchain-based security, IEEE Trans. Serv. Comput. 13 (4) (2020) 625-638.

[21] T. Wang, H. Luo, W. Jia, A. Liu, M. Xie, MTES: an intelligent trust evaluation scheme in sensor-cloud-enabled Industrial Internet of Things, IEEE Trans. Ind. Inf. 16 (3) (2020) 2054-2062.

[22] S. Pinto, T. Gomes, J. Pereira, J. Cabral, A. Tavares, IIoTEED: an enhanced, trusted execution environment for industrial IoT edge devices, IEEE Internet Comput 21 (1)(2017) 40-47.

[23] Y. He, C. Liang, F.R. Yu, Z. Han, Trust-based social networks with computing, caching and communications: a deep reinforcement learning approach, IEEE Trans. Netw. Sci. Eng. 7 (1) (2020) 66-79.

[24] J. Wang, X. Wei, J. Fan, Q. Duan, J. Liu, Y. Wang, Request pattern change-based cache pollution attack detection and defense in edge computing, Digit. Commun. Netw. (early access 2022).

[25] J. Ni, K. Zhang, X. Lin, X. Shen, Securing fog computing for Internet of Things applications: challenges and solutions, IEEE Commun. Surv. Tutor. 20 (1) (2018) 601-628.

[26] A. Alrawais, A. Alhothaily, C. Hu, X. Cheng, Fog computing for the Internet of Things: security and privacy issues, IEEE Internet Comput 21 (2) (2017) 34-42.

[27] Y. Jia, F. Zhong, A. Alrawais, B. Gong, X. Cheng, FlowGuard: an intelligent edge defense mechanism against IoT DDoS attacks, IEEE Internet Things J. 7 (10) (2020) 9552-9562.

[28] R. Doriguzzi-Corin, S. Millar, S. Scott-Hayward, J. Martínez-del-Rincón, D. Siracusa, Lucid: a practical, lightweight deep learning solution for DDoS attack detection, IEEE Trans. Netw. Serv. Manag. 17 (2) (2020) 876-889.

[29] A. Diro, N. Chilamkurti, Leveraging LSTM networks for attack detection in fog-to-things communications, IEEE Commun. Mag. 56 (9) (2018) 124-130.

[30] A. Abeshu, N. Chilamkurti, Deep learning: the frontier for distributed attack detection in fog-to-things computing, IEEE Commun. Mag. 56 (2) (2018) 169-175.

[31] L. Xiao, X. Wan, C. Dai, X. Du, X. Chen, M. Guizani, Security in mobile edge caching with reinforcement learning, IEEE Wireless Commun. 25 (3) (2018) 116-122.

[32] M. Song, et al. Analyzing user-level privacy attack against federated learning, J. Sel. Areas Commun. 38 (10) (2020) 2430-2444.

[33] J. Zhou et al., A differentially private federated learning model against poisoning attacks in edge computing, IEEE Trans. Dependable Secure Comput. (early access 2022).

[34] Y. Xiao, Y. Jia, C. Liu, X. Cheng, J. Yu, W. Lv, Edge computing security: state of the art and challenges, Proc. IEEE 107 (8) (2019) 1608-1631.

[35] S. Xu, Y. Qian, R.Q. Hu, Edge intelligence assisted gateway defense in cyber security, IEEE Netw. 34 (4) (2020) 14-19.

[36] M. Eskandari, Z.H. Janjua, M. Vecchio, F. Antonelli, Passban IDS: an intelligent anomaly-based intrusion detection system for IoT edge devices, IEEE Internet Things J. 7 (8) (2020) 6882-6897.

[37] N. Moustafa, K.R. Choo, I. Radwan, S. Camtepe, Outlier dirichlet mixture mechanism: adversarial statistical learning for anomaly detection in the fog, IEEE Trans. Inf. Forensics Secur. 14 (8) (2019) 1975-1987.

[38] S. Xu, Y. Qian, R.Q. Hu, Data-driven edge intelligence for robust network anomaly detection, IEEE Trans. Netw. Sci. Eng. 7 (3) (2020) 1481-1492.

[39] I. Hafeez, M. Antikainen, A.Y. Ding, S. Tarkoma, IoT-KEEPER: detecting malicious IoT network activity using online traffic analysis at the edge, IEEE Trans. Netw. Serv. Manag. 17 (1) (2020) 45-59.

[40] V. Mothukuri, P. Khare, R.M. Parizi, S. Pouriyeh, A. Dehghantanha, G. Srivastava, Federated-learning-based anomaly detection for IoT security attacks, IEEE Internet Things J. 9 (4) (2022) 2545-2554.

[41] A. Khelloufi, et al., A social-relationships-based service recommendation system for SIoT devices, IEEE Internet Things J. 8 (3) (2021) 1859-1870.

[42] T. van Erven, P. Harremos, Rényi divergence and Kullback-Leibler divergence, IEEE Trans. Inf. Theor. 60 (7) (2014) 3797-3820.

[43] J. Schrittwieser, et al., Mastering Atari, Go, chess and shogi by planning with a learned model, Nature 588 (1) (2020) 604-609.

[44] J. Gabirondo-López, J. Egaña, J. Miguel-Alonso, R. Orduna Urrutia, Towards autonomous defense of SDN networks using MuZero based intelligent agents, IEEE Access 9 (1) (2021) 107184-107199.

[45] H. Shuai, H. He, Online scheduling of a residential microgrid via Monte-Carlo tree search and a learned model, IEEE Trans. Smart Grid 12 (2) (2021) 1073-1087.

[46] F.T. Liu, K.M. Ting, Z. Zhou, Isolation forest, in: Eighth IEEE International Conference on Data Mining, IEEE, 2008, pp. 413-422.

[47] E. Cho, S.A. Myers, J. Leskovec, Friendship and mobility: user movement in location-based social networks, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2011, pp. 1-9.

[48] S. Sagar, A. Mahmood, Q.Z. Sheng, S.A. Siddiqui, SCaRT-SIoT: towards a scalable and robust trust platform for social internet of things: demo abstract,in:Proceedings of the 18th Conference on Embedded Networked Sensor Systems (SenSys), ACM, 2020, pp. 635-636.

[49] P. Yang, Y. Yang, P. Zhang, D. Wu, R. Wang, Sensitivity enhanced edge-cloud collaborative trust evaluation in social Internet of Things, IEICE Trans. Commun. 105 (9) (2022) 1053-1062.

[50] I. Chen, J. Guo, D. Wang, J.J.P. Tsai, H. Al-Hamadi, I. You, Trust-based service management for mobile cloud IoT systems, IEEE Trans. Netw. Serv. Manag. 16 (1)(2019) 246-263.

[51] M. Nitti, R. Girau, L. Atzori, Trustworthiness management in the social internet of things, IEEE Trans. Knowl. Data Eng. 26 (5) (2014) 1253-1266.