Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Learning-Based Secure Mobile Offloading

  • Liang XiaoEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_122-1

Definition

Learning-based secure offloading strategy applies the reinforcement learning algorithms to derive the optimal data offloading and defense policy for the mobile devices to counter the potential smart attackers in a mobile computing system, without any preliminary knowledge of the environmental parameters and the attack model.

Historical Background

With the proliferation of cloud-based mobile services, mobile devices such as smartphones and tablets can offload their applications and data to the cloud to improve user experience in terms of longer battery lifetime, larger data storage, faster processing speed, and more powerful security services (Xiao et al., 2017). However, data offloading to the cloud via access points (APs) or base stations (BSs) is vulnerable to various types of attacks, such as spoofing, eavesdropping, and jamming (Kumar and Lu, 2010). A smart attacker can use smart and programmable radio devices such as Universal Software Radio Peripherals (USPRs) or the...

This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant 61671396.

References

  1. Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(4):974–983CrossRefGoogle Scholar
  2. He D, Chen C, Chan S, Bu J (2012) Secure and efficient handover authentication based on bilinear pairing functions. IEEE Trans Wirel Commun 11(1):48–53CrossRefGoogle Scholar
  3. Hu P, Li H, Fu H, Cansever D, Mohapatra P (2015) Dynamic defense strategy against advanced persistent threat with insiders. In: Proceedings of IEEE international conference on computer communications (INFOCOM), pp 747–755Google Scholar
  4. Kumar K, Lu YH (2010) Cloud computing for mobile users: can offloading computation save energy? IEEE Comput 43(4):51–56CrossRefGoogle Scholar
  5. Li Z, Wang C, Xu R (2001) Computation offloading to save energy on handheld devices: a partition scheme. In: Proceedings of ACM international conference on compilers, architecture, and synthesis for embedded systems, pp 238–246Google Scholar
  6. Mukherjee A, Swindlehurst AL (2010) Optimal strategies for countering dual-threat jamming/eavesdropping-capable adversaries in MIMO channels. In: Proceedings of IEEE military communications conference, pp 1695–1700Google Scholar
  7. Murphy P, Sabharwal A, Aazhang B (2006) Design of WARP: a wireless open-access research platform. In: IEEE signal processing conference, 2006 14th European, pp 1–5Google Scholar
  8. Sutton R, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, CambridgezbMATHGoogle Scholar
  9. Wang Y, Lin X, Pedram M (2013) A nested two stage game-based optimization framework in mobile cloud computing system. In: Proceedings of IEEE international symposium on service oriented system engineering, pp 494–502Google Scholar
  10. Xiao L (2015) Anti-jamming transmissions in cognitive radio networks. Springer. ISBN:978-3-319-24290-3CrossRefGoogle Scholar
  11. Xiao L, Greenstein LJ, Mandayam NB, Trappe W (2009) Channel-based spoofing detection in frequency-selective rayleigh channels. IEEE Trans Wirel Commun 8(12):5948–5956CrossRefGoogle Scholar
  12. Xiao L, Xie C, Chen T, Dai H, Poor HV (2016) A mobile offloading game against smart attacks. IEEE Access 4:2281–2291CrossRefGoogle Scholar
  13. Xiao L, Li Y, Huang X, Du X (2017) Cloud-based malware detection game for mobile devices with offloading. IEEE Trans Mobile Comput 16(10):2742–2750CrossRefGoogle Scholar
  14. Xiao L, Chen T, Xie C, Dai H, Poor HV (2018a) Mobile crowdsensing games in vehicular networks. IEEE Trans Veh Technol 62(2):1535–1545CrossRefGoogle Scholar
  15. Xiao L, Li Y, Han G, Dai H, Poor HV (2018b) A secure mobile crowdsensing game with deep reinforcement learning. IEEE Trans Inf Forensics Secur 13(1):35–47CrossRefGoogle Scholar
  16. Xiao L, Wan X, Dai C, Du X, Chen X, Guizani M (2018c) Security in mobile edge caching with reinforcement learning. IEEE Wirel Comm 25(3):116–122 MagGoogle Scholar
  17. Xiao L, Wan X, Lu X, Zhang Y, Wu D (2018d) IoT security techniques based on machine learning. IEEE Sig Process Mag 35(5):41–49CrossRefGoogle Scholar
  18. Xiao L, Xu D, Mandyam N, Poor HV (2018e) Attacker-centric view of a detection game against advanced persistent threats. IEEE Trans Mobile Comput 17(11):2512–2523CrossRefGoogle Scholar
  19. Yang Z, Niyato D, Wang P (2015) Offloading in mobile cloudlet systems with intermittent connectivity. IEEE Trans Mobile Comput 14(12):2516–2529CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Communication EngineeringXiamen UniversityXiamenChina

Section editors and affiliations

  • Yingying Chen

There are no affiliations available