publication . Preprint . 2018

IoT Security Techniques Based on Machine Learning

Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di;
Open Access English
  • Published: 18 Jan 2018
Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that nee...
free text keywords: Computer Science - Cryptography and Security
Download from
30 references, page 1 of 2

[1] X. Li, R. Lu, X. Liang, and X. Shen, “Smart community: An Internet of Things application,” IEEE Commun. Magazine, vol. 49, no. 11, pp. 68-75, Nov. 2011.

[2] Z. Sheng, S. Yang, Y. Yu, and A. Vasilakos, “A survey on the IETF protocol suite for the Internet of Things: Standards, challenges, and opportunities,” IEEE Wireless Commun., vol. 20, no. 6, pp. 91-98, Dec. 2013. [OpenAIRE]

[3] X. Liu, M. Zhao, S. Li, F. Zhang, and W. Trappe, “A security framework for the Internet of Things in the future Internet architecture,” Future Internet, vol. 9, no. 3, pp. 1-28, Jun. 2017.

[4] I. Andrea, C. Chrysostomou, and G. Hadjichristofi, “Internet of Things: Security vulnerabilities and challenges,” in Proc. IEEE Symposium on Computers and Commun, pp. 180-187, Larnaca, Cyprus, Feb. 2015.

[5] R. Roman, J. Zhou, and J. Lopez, “On the features and challenges of security and privacy in distributed Internet of Things,” Computer Networks, vol. 57, no. 10, pp. 2266-2279, Jul. 2013.

[6] S. Chen, H. Xu, D. Liu, and B. Hu, “A vision of IoT: Applications, challenges, and opportunities with china perspective,” IEEE Internet of Things Journal, vol. 1, no. 4, pp. 349-359, Jul. 2014.

[7] J. Zhou, Z. Cao, X. Dong, and A. V. Vasilakos, “Security and privacy for cloud-based IoT: Challenges,” IEEE Commun. Magazine, vol. 55, no. 1, pp. 26-33, Jan. 2017. [OpenAIRE]

[8] L. Xiao, Y. Li, G. Han, G. Liu, and W. Zhuang, “PHY-layer spoofing detection with reinforcement learning in wireless networks,” IEEE Trans. Vehicular Technology, vol. 65, no. 12, pp. 10037-10047, Dec. 2016.

[9] M. Abu Alsheikh, S. Lin, D. Niyato, and H. P. Tan, “Machine learning in wireless sensor networks: Algorithms, strategies, and applications,” IEEE Commun. Surveys and Tutorials, vol. 16, no. 4, pp. 1996-2018, Apr. 2014.

[10] L. Xiao, C. Xie, T. Chen, and H. Dai, “A mobile offloading game against smart attacks,” IEEE Access, vol. 4, pp. 2281-2291, May 2016.

[11] L. Xiao, Y. Li, X. Huang, and X. J. Du, “Cloud-based malware detection game for mobile devices with offloading,” IEEE Trans. Mobile Computing, vol. 16, no. 10, pp. 2742-2750, Oct. 2017.

[12] M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, “Machine learning methods for attack detection in the smart grid,” IEEE Trans. Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1773-1786, Mar. 2015.

[13] J. W. Branch, C. Giannella, B. Szymanski, R. Wolff, and H. Kargupta, “In-network outlier detection in wireless sensor networks,” Knowledge and Information Systems, vol. 34, no. 1, pp. 23-54, Jan. 2013.

[14] F. A. Narudin, A. Feizollah, N. B. Anuar, and A. Gani, “Evaluation of machine learning classifiers for mobile malware detection,” Soft Computing, vol. 20, no. 1, pp. 343-357, Jan. 2016. [OpenAIRE]

[15] A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Commun. Surveys and Tutorials, vol. 18, no. 2, pp. 1153-1176, Oct. 2015.

30 references, page 1 of 2
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue