Machine Learning Algorithms for IoT Security

1. O. Novo, N. Beijar, and M. Ocak (2015), ―Capillary Networks - Bridging the Cellular and loT Worlds ,‖ IEEE World Forum on Internet
of Things (WF-IoT), vol. 1, pp. 571–578.
2. 2 .J. Granjal, E. Monteiro, and J. S. Silva (2015), ―Security for the internet of things: A survey of existing protocols and open research issues,‖ IEEE Communications Surveys Tutorials, vol. 17, pp. 1294–1312.
3. M. at. El (2018), ―Machine Learning for Internet of Things Data Analysis:A Survey ,‖ Journal of Digital Communications and Networks,Elsevier, vol. 1, pp. 1–56.
4. J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng (2016), ―A survey of machine learning for big data processing,‖ EURASIP Journal of
Advance Signal Process.
5. L. Deng, D. Li, X. Yao, D. Cox, and H. Wang (2018), ―Mobile network intrusion detection for iot system based on transfer learning
algorithm,‖ Cluster Computing.
6. R. Doshi, N. Apthorpe, and N. Feamster (2018), ―Machine learning ddos detection for consumer internet of things devices,‖ in
2018 IEEE Security and Privacy Workshops (SPW), pp. 29–35.
7. F. Hussain, A. Anpalagan, A. S. Khwaja, and M. Naeem (2015), ―Resource Allocation and Congestion Control in Clustered M2M
Communication using Q-Learning,‖ Wiley Transactions on Emerging Telecommunica-tions Technologies.
8. M. S. Alam and S. T. Vuong (2013), ―Random forest classification for detecting android malware,‖ in 2013 IEEE International
Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing,pp. 663–669.
9. W. Zhou and B. Yu (2018), ―A cloud-assisted malware detection and suppres-sion framework for wireless multimedia system in iot
based on dynamic differential game,‖ China Communications, vol. 15, pp. 209–223.
10. H.-S. Ham, H.-H. Kim, M.-S. Kim, and M.-J. Choi (2018), ―Linear svm-based android malware
11. H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo (2018), ―A deep recurrent neural network based approach
for internet of things malware threat hunting,‖Future Generation Computer Systems, vol. 85,pp 88 – 96.
12. E. B. Karbab, M. Debbabi, A. Derhab, and D. Mouheb (2018), ―Maldozer: Automatic framework for android malware detection
using deep learn-ing,‖ Digital Investigation, vol. 24, pp. S48 – S59.
13. J. Su, D. V. Vargas, S. Prasad, D. Sgandurra, Y. Feng, and K. Sakurai (2018), ―Lightweight classification of iot malware based on
image recognition,‖ CoRR, vol. abs/1802.03714.
14. N. An, A. Duff, G. Naik, M. Faloutsos, S. Weber, and S. Mancoridis (2017), ―Behavioral anomaly detection of malware on home
routers,‖ in 2017 12th International Conference on Malicious and Unwanted Software (MALWARE), pp. 47–54.
15. A. Azmoodeh, A. Dehghantanha, and K. R. Choo (2018), ―Robust malware de-tection for internet of (battlefield) things devices using deep eigenspace learning,‖ IEEE Transactions on Sustainable Computing, pp. 1–1.
16. A. LHeureux, K. Grolinger, H. F. Elyamany, and M. A. M. Capretz (2017), ―Machine Learning With Big Data: Challenges and
Approaches,‖ IEEE Access, vol. 5, pp. 7776 – 7797.
17. T. E. Bogale, X. Wang, and L. B. Le (2018), ―Machine Intelligence Techniques for Next- Generation Context-Aware Wireless
Networks,‖ Arxiv, vol. 19,1–10..