Abstract
A large amount of real-time data, including user privacy information, control commands, and other sensitive data, are transmitted in edge computing networks. It requires high-speed and reliable data transmission in dynamic edge computing networks. Traditional methods with passive defense cannot cope with the covert and complicated attacks. Edge computing networks require active defense during data transmission. Existing active defense methods based on dynamic network ignore the connectivity and link quality reduced by attacks and do not adjust defense positively. To maximize the defense revenue in moving adjustment strategy, this paper proposes the model of active defense for edge computing network data interaction. In this model, the network topology mimic association protocol is designed to associate multi-paths and multi-parameters automatically. On one hand, considering the transmission reliability and defensive revenue reduction caused by dynamic network transformation, a real-time multi-feature anomaly detection algorithm based on Non-extensive entropy and Renyi cross entropy is proposed. Based on this, a moving communication path alliance can be constructed pseudo-randomly. On the other hand, this paper proposes a Hidden Markov based state prediction model and a mimic transformation strategy for The Network Topology Mimic Association Graph based on predicted states. Combining these two ways improves the data transmission service quality of the active defense technology in edge computing networks. Experiments are carried in simulated power networks. The results show that our method outperforms the popular methods in terms of transmission efficiency, reliability, and anti-attack performance.
Similar content being viewed by others
References
Roman R, Lopez J, Mambo M (2018) Mobile edge computing, Fog et al.: a survey and analysis of security threats and challenges. Futur Gener Comput Syst 78:680–698
Chen Y, Zhang Y, Maharjan S (2017) Deep learning for secure mobile edge computing. arXiv preprint arXiv:1709.08025
Ai Y, Peng M, Zhang K (2018) Edge computing technologies for internet of things: a primer. Digit Commun Netw 4(2):77–86
Gershenfeld N, Krikorian R, Cohen D (2004) The internet of things. Sci Am 291(4):76–81
Li H, Ota K, Dong M (2018) Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw 32(1):96–101
Yang H (2016) Method for behavior-prediction of APT attack based on dynamic Bayesian game. In: 2016 IEEE international conference on cloud computing and big data analysis (ICCCBDA), pp 177–182, Chengdu, China
Wan J et al (2018) Toward dynamic resources management for IoT-based manufacturing. IEEE Commun Mag 56(2):52–59
Wang J, Cao J, Ji S, Park JH (2017) Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks. J Supercomput 73(7):3277–3290
Yin Y, Zhang W, Xu Y, Zhang H, Mai Z, Yu L (2019) QoS prediction for Mobile edge service recommendation with auto-encoder. IEEE Access 7:1–1
Yin Y, Chen L, Xu Y, Wan J, Zhang H, Mai Z (2019) QoS prediction for service recommendation with deep feature learning in edge computing environment. Mob Networks Appl:1–11
Gao H, Zhang K, Yang J, Wu F, Liu H (2018) Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks. Spec Collect Artic Int J Distrib Sens Netw 14(2):2018
Gao H, Huang W, Yang X, Duan Y, Yin Y (2018) Toward service selection for workflow reconfiguration: an interface-based computing solution. Futur Gener Comput Syst 87:298–311
Dunlop M, Groat S, Urbanski W, Marchany R, Tront J (2011) MT6D: a moving target IPv6 defense. In: 2011 – MILCOM 2011 military communications conference, pp 1321–1326, Baltimore, MD, USA
Atighetchi M, Pal P, Webber F, Jones C (2003) Adaptive use of network-centric mechanisms in cyber-defense. In: Second IEEE international symposium on network computing and applications. NCA 2003, pp 179–188, Cambridge, MA, USA
Badishi G, Herzberg A, Keidar I (2007) Keeping denial-of-service attackers in the dark. IEEE Transactions on Dependable and Secure Computing 4(3):191–204
Antonatos S, Akritidis P, Markatos EP, Anagnostakis KG (2005) Defending against Hitlist Worms using network address space randomization. Computer Networks 51(12):3471–3490
Jafarian JH, Al-Shaer E, Duan Q (2012) OpenFlow random host mutation: transparent moving target defense using software defined networking. Proceedings of the first workshop on Hot topics in software defined networks. ACM, pp 127–132
Dunlop M, Groat S, Urbanski W, Marchany R, Tront J (Jul. 2012) The blind man’s bluff approach to security using IPv6. IEEE Secur Priv Mag 10(4):35–43
Jafarian JH, Al-Shaer E, Duan Q (2014) Spatio-temporal address mutation for proactive cyber agility against sophisticated attackers. Proceedings of the First ACM Workshop on Moving Target Defense. ACM, pp 69–78
MacFarland DC, Shue CA (2015) The SDN shuffle. In: Proceedings of the second ACM workshop on moving target defense – MTD ‘15, pp 37–41, Denver, Colorado, US
Skowyra R, Bauer K, Dedhia V, Okhravi H (2016) Have no phear: Networks without identifiers. Proceedings of the 2016 ACM Workshop on Moving Target Defense. ACM, pp 3–14
Sun J, Sun K (2016) DESIR: decoy-enhanced seamless IP randomization. In: IEEE INFOCOM 2016 – the 35th annual IEEE international conference on computer communications, pp 1–9
Jiangxing Wu. C. M. Defense, “Research on Cyber Mimic Defense,”. J Cyber Secur vol. 1, no. 4, pp. 1–10, 2016
Gao H, Miao H, Liu L, Kai J, Zhao K (2018) Automated quantitative verification for service-based system design: a visualization transform tool perspective. Int J Softw Eng Knowl Eng 28(10):1369–1397
Gao H, Mao S, Huang W, Yang X (2018) Applying probabilistic model checking to financial production risk evaluation and control: a case study of Alibaba’s Yu’e Bao. IEEE Trans Comput Soc Syst 5(3):785–795
Haggerty J, Shi Q, Merabti M (2002) Beyond the perimeter: the need for early detection of denial of service attacks. In: Proceedings of 18th annual computer security applications conference, pp 413–422, Las Vegas, NV, USA
Zhang J, Gunter CA (2010) Application-aware secure multicast for power grid communications. In: 2010 first IEEE international conference on smart grid communications, pp 339–344, Gaithersburg, MD, USA
Yin Y, Chen L, Xu Y, Wan J (2018) Location-aware service recommendation with enhanced probabilistic matrix factorization. IEEE Access 6:62815–62825
Yin Y, Yu F, Xu Y, Yu L, Mu J (2017) Network location-aware service recommendation with random walk in cyber-physical systems. Sensors 17(9):2059
Liang W et al (2018) A security situation prediction algorithm based on HMM in Mobile network. Wirel Commun Mob Comput 2018:1–11
Wan M, Yao J, Jing Y, Jin X (2018) Event-based anomaly detection for non-public industrial communication protocols in SDN-based control systems. CMC 55(3):447–463
Yan Q, Huang W, Luo X, Gong Q, Yu FR (2018) A multi-level DDoS mitigation framework for the industrial internet of things. IEEE Commun Mag 56(2):30–36
Bereziński P, Jasiul B, Szpyrka M, Bereziński P, Jasiul B, Szpyrka M (2015) An entropy-based network anomaly detection method. Entropy 17(4):2367–2408
Prasanth Vaidya S, Chandra Mouli PVSSR (2017) A robust semi-blind watermarking for color images based on multiple decompositions. Multimed Tools Appl 76(24):25623–25656
Zhao C, Jia C (2013) Research on spatial adaptive strategy of end-hopping system. In: 2013 fourth international conference on emerging intelligent data and web technologies, pp 661–666, Xi\"an, Shaanxi, China
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This paper supported by The Fundamental Research Funds for the Central Universities (No.30918012204), Jiangsu province key research and development program(BE2017739), 2018 Jiangsu Province Major Technical Research Project "Information Security Simulation System”(BE2017100), Military Common Information System Equipment Pre-research Special Technical Project (315075701). Industrial Internet Innovation and Development Project in 2019 - Industrial Internet Security On-Site Emergency Detection Tool Project.
Rights and permissions
About this article
Cite this article
Wang, S., Li, Q., Hou, J. et al. Active Defense by Mimic Association Transmission in Edge Computing. Mobile Netw Appl 25, 725–742 (2020). https://doi.org/10.1007/s11036-019-01446-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-019-01446-w