Starlet: Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

Abstract

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things. To guarantee the network's overall security, we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit. Firstly, we propose the method for dynamic setting of node defense resource thresholds to obtain the defender (attacker) benefit function of edge servers (nodes) and distribution. Secondly, we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes. Subsequently, we use the decomposability and Lipschitz continuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically. Finally, experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

Keywords

Internet of things / Defense resource sharing / Multi-armed bandits / Defense resource allocation

References

[1] S. Cresci, A. Cimino, M. Avvenuti, M. Tesconi, F. Dell’Orletta, Real-world witness detection in social media via hybrid crowdsensing, Proceedings of International AAAI Conference on Web and Social Media, 2018. https://doi.org/10.1609/icwsm. v12i1.15072.

[2] G. Gao, J. Wu, M. Xiao, G. Chen, Combinatorial multi-armed bandit based unknown worker recruitment in heterogeneous crowdsensing, in: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, IEEE, 2020, pp. 179-188.

[3] W. Wang, Y. Wang, P. Duan, T. Liu, X. Tong, Z. Cai, A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing, IEEE Trans. Mobile Comput. 10 (22) (2023) 5625-5642.

[4] G. Raja, P. Dhanasekaran, S. Anbalagan, A. Ganapathisubramaniyan, A.K. Bashir, Sdn-enabled traffic alert system for iov in smart cities, in: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2020, pp. 1093-1098.

[5] Z. Ning, S. Sun, X. Wang, L. Guo, S. Guo, X. Hu, B. Hu, R. Kwok, Blockchain-enabled intelligent transportation systems: a distributed crowdsensing framework, IEEE Trans. Mobile Comput. 12 (21) (2022) 4201-4217.

[6] C. Zheng, X. Fan, C. Wang, J. Qi, Gman: a graph multi-attention network for traffic prediction,in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 1234-1241.

[7] T. Shi, Z. Cai, J. Li, H. Gao, J. Chen, M. Yang, Services management and distributed multihop requests routing in mobile edge networks, IEEE/ACM Trans. Netw. 2 (31)(2023) 497-510.

[8] Z. Lu, Y. Wang, Y. Li, X. Tong, C. Mu, C. Yu, Data-driven many-objective crowd worker selection for mobile crowdsourcing in industrial IoT, IEEE Trans. Ind. Inf. 1 (19) (2023) 531-540.

[9] Z. Cai, T. Shi, Distributed query processing in the edge-assisted iot data monitoring system, IEEE Internet Things J. 8 (16) (2020) 12679-12693.

[10] T. Zhu, T. Shi, J. Li, Z. Cai, X. Zhou, Task scheduling in deadline-aware mobile edge computing systems, IEEE Internet Things J. 6 (3) (2018) 4854-4866.

[11] L. Yu, H. Shen, Z. Cai, L. Liu, C. Pu, Towards bandwidth guarantee for virtual clusters under demand uncertainty in multi-tenant clouds, IEEE Trans. Parallel Distr. Syst. 29 (2) (2017) 450-465.

[12] W. Xie, X. Yu, Y. Zhang, H. Wang, An improved shapley value benefit distribution mechanism in cooperative game of cyber threat intelligence sharing, in: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2020, pp. 810-815.

[13] J. Hou, L. Sun, T. Shu, H. Li, The value of traded target information in security games, IEEE/ACM Trans. Netw. 29 (4) (2021) 1853-1866.

[14] D. Ye, T. Zhu, S. Shen, W. Zhou, A differentially private game theoretic approach for deceiving cyber adversaries, IEEE Trans. Inf. Forensics Secur. 16 (2020) 569-584.

[15] X. Liu, T.J. Lim, J. Huang, Optimal byzantine attacker identification based on game theory in network coding enabled wireless ad hoc networks, IEEE Trans. Inf. Forensics Secur. 15 (2020) 2570-2583.

[16] M. Li, L. Tran-Thanh, X. Wu,Defending with shared resources on a network, in:Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 2111-2118.

[17] T. Huang, W. Shen, D. Zeng, T. Gu, R. Singh, F. Fang, Green security game with community engagement, arXiv preprint arXiv:2002.09126.

[18] K. Huang, C. Zhou, Y. Qin, W. Tu, A game-theoretic approach to cross-layer security decision-making in industrial cyber-physical systems, IEEE Trans. Ind. Electron. 67 (3) (2019) 2371-2379.

[19] W. Shen, W. Chen, T. Huang, R. Singh, F. Fang, When to follow the tip: security games with strategic informants,in:Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, ACM, 2020, pp. 371-377.

[20] L.-X. Yang, P. Li, Y. Zhang, X. Yang, Y. Xiang, W. Zhou, Effective repair strategy against advanced persistent threat: a differential game approach, IEEE Trans. Inf. Forensics Secur. 14 (7) (2018) 1713-1728.

[21] R. Kleinberg, A. Slivkins, E. Upfal, Bandits and experts in metric spaces, J. ACM 66 (4) (2019) 1-77.

[22] W. Chen, Y. Wang, Y. Yuan, Q. Wang, Combinatorial multi-armed bandit and its extension to probabilistically triggered arms, J. Mach. Learn. Res. 17 (1) (2016) 1746-1778.

[23] J. Shweta, G. Sujit,A multiarmed bandit based incentive mechanism for a subset selection of customers for demand response in smart grids, in:Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 2046-2053.

[24] H. Xu, Y. Liu, W.C. Lau, R. Li, Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints, IJCAI, 2020, pp. 2554-2560.

[25] L. Xu, E. Bondi, F. Fang, A. Perrault, K. Wang, M. Tambe, Dual-mandate patrols: multi-armed bandits for green security,in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, 2021, pp. 14974-14982.

[26] K. Wang, A. Zhang, H. Sun, B. Wang, Analysis of recent deep-learning-based intrusion detection methods for in-vehicle network, IEEE Trans. Intell. Transport. Syst. 2 (24) (2023) 1843-1854.

[27] Z. Liu, H. Wang, F. Shen, K. Liu, L. Chen,Incentivized exploration for multi-armed bandits under reward drift, in:Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 4981-4988.

[28] G. Bansal, B. Sikdar, Security service pricing model for uav swarms: a stackelberg game approach, in: IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2021, pp. 1-6.

[29] X. Ma, B. An, M. Zhao, X. Luo, L. Xue, Z. Li, T.T. Miu, X. Guan, Randomized security patrolling for link flooding attack detection, IEEE Trans. Dependable Secure Comput. 17 (4) (2019) 795-812.

[30] W. Jones, R.E. Wilson, A. Doufexi, M. Sooriyabandara, A pragmatic approach to clear channel assessment threshold adaptation and transmission power control for performance gain in csma/ca wlans, IEEE Trans. Mobile Comput. 19 (2) (2019) 262-275.

[31] H. Liu, X. Xu, J.-A. Lu, G. Chen, Z. Zeng, Optimizing pinning control of complex dynamical networks based on spectral properties of grounded laplacian matrices, IEEE Trans. Syst. Man, and Cybern.: Systems 51 (2) (2018) 786-796.