Efficient Cognitive Fog Computing for Classification of Network Cyberattacks Using Machine Learning

Authors

  • Prof. A. V. Deorankar  Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India
  • Shiwani S. Thakare  Computer Science and Engineering, Government College of Engineering, Amravati, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT206444

Keywords:

Cybersecurity, Fog layer, IoT, Anomaly detection.

Abstract

IoT is the network which connects and communicates with billions of devices through the internet and due to the massive use of IoT devices, the shared data between the devices or over the network is not confidential because of increasing growth of cyberattacks. The network traffic via loT systems is growing widely and introducing new cybersecurity challenges since these loT devices are connected to sensors that are directly connected to large-scale cloud servers. In order to reduce these cyberattacks, the developers need to raise new techniques for detecting infected loT devices. In this work, to control over this cyberattacks, the fog layer is introduced, to maintain the security of data on a cloud. Also the working of fog layer and different anomaly detection techniques to prevent the cyberattacks has been studied. The proposed AD-IoT can significantly detect malicious behavior using anomalies based on machine learning classification before distributing on a cloud layer. This work discusses the role of machine learning techniques for identifying the type of Cyberattacks. There are two ML techniques i.e. RF and MLP evaluated on the USNW-NB15 dataset. The accuracy and false alarm rate of the techniques are assessed, and the results revealed the superiority of the RF compared with MLP. The Accuracy measures by classifiers are 98 and 53 of RF and MLP respectively, which shows a huge difference and prove the RF as most efficient algorithm with binary classification as well as multi- classification.

References

  1. Abeshu and N. Chilarnkurti, "Deep learning: the frontier for distributed attack detection in fog-to-things computing," IEEE CommunicationsMagazine, vol. 56, no. 2, pp. 169-175, 2018.
  2. T. A. Tang, L. Mhamdi, D. McLernon, S. A. R. Zaidi, and M. Ghogho, "Deep learning approach for network intrusion detection in software defined networking," in Wireless Networks and Mobile Communications (WINCOM), 2016 International Conference on. IEEE, 2016, pp. 258-263.
  3. B. Zarpelao, R. S. Miani, C. T. Kawakani, and S. C. de Alvarenga, "A survey of intrusion detection in internet of things;' Journal of Networkand Computer Applications, vol. 84, pp. 25-37, 2017.
  4. M. M. Hossain, M. Fotouhi, and R. Hasan, "Towards an analysis of security issues, challenges, and open problems in the internet of things," in Services (SERVICES), 2015 IEEE World Congress on. IEEE, 2015, pp. 21-28.
  5. H. Habibzadeh, T. Soyata, B. Kantarci, A. Boukerche, and C. Kaptan, "Sensing, communication and security planes: A new challenge for a smart city system design r," Computer Networks, vol. 144, pp. 163- 200, 2018.
  6. N. Moustafa and J. Slay, "Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)," in Military Communications and Information Systems Conference (MilCIS),2015. IEEE, 2015, pp. 1-6.
  7. N. Koroniotis, N. Moustafa, E. Sitnikova, and J. Slay, "Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques," in Mobile Networks and Management:9th International Conference, MONAMI 2017, Melbourne, Australia,December 13-15, 2017, Proceedings, vol. 235. Springer, 2018, pp. 30--44.
  8. M. Nobakht, V. Sivaraman, and R. Boreli, "A host-based intrusion detection and mitigation framework for smart home iot using openflow," in Availability, Reliability and Security (ARES), 2016 11th InternationalConference on. IEEE, 2016, pp. 147-156.
  9. H. Summerville, K. M. Zach, and Y. Chen, "Ultra-lightweight deep packet anomaly detection for internet of things devices;' in Computing and Communications Conference (IPCCC), 2015 IEEE 34th International Performance. IEEE, 2015, pp. 1-8
  10. S. Garg, K. Kaur, N. Kumar, S. Batra, and M. S. Obaidat, "Hyclass: Hybrid classification model for anomaly detection in cloud environment," in 2018 IEEE International Conference on Communications (ICC). IEEE, 2018, pp. 1-7.
  11. M. Ahmed, A. N. Mahmood, and J. Hu, "A survey of network anomaly detection techniques," Journal of Network and Computer Applications, vol. 60, pp. 19-31, 2016. [l] J. Howell. Number of connected iot devices will surge to 125 billion by 2030, ihs markit says - ihs technology. [Online]. Available: https://technology.ihs.com/596542, last accessed: 11/07/2018.
  12. Borgia, "The internet of things vision: Key features, applications and open issues," Computer Communications, vol. 54, pp. 1-31, 2014.
  13. Restuccia, S. D'Oro, and T. Melodia, "Securing the internet of things: New perspectives and research challenges," IEEE Internet of ThingsJournal, vol. 1, no. 1, pp. 1-14, 2018.
  14. J. A. Stankovic, "Research directions for the internet of things," IEEE Internet of Things Journal, vol. 1, no. 1, pp. 3-9, 2014.
  15. M. Antonakakis, T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, Z. Durumeric, J. A. Halderman, L. Invernizzi, M. Kallitsis et al., "Understanding the mirai botnet;' in USENIX Security Symposium, 2017, pp. 1092-1110.
  16. J. Santos, P. Leroux, T. Wauters, B. Volckaert, and F. D. Turck, "Anomaly detection for smart city applications over 5g low power wide area networks," in NOMS 2018 - 2018 IEEE/IFIP Network Operationsand Management Symposium, 2018, PP-1.
  17. Yousefpour, G. Ishigaki, and J. P. Jue, "Fog computing: Towards minimizing delay in the internet of things," in Edge Computing (EDGE),2017 IEEE International Conference on. IEEE, 2017, pp. 17-2.
  18. R. Roman, J. Zhou, and J. Lopez, "On the features and challenges of security and privacy in distributed internet of things," ComputerNetworks, vol. 57, no. 10, pp. 2266-2279, 2013.
  19. S. Prabavathy, K. Sundarakantham, and S. M. Shalinie, "Design of cognitive fog computing for intrusion detection in internet of things," Journal of Communications and Networks, vol. 20, no. 3, pp. 291-298, 2018.
  20. D. Oh, D. Kim, and W.W. Ro, "A malicious pattern detection engine for N. Moustafa and J. Slay, "The evaluation of network anomaly detection systems: Statistical analysis of the unsw-nb15 data set and the comparison with the kdd99 data set," Information Security Journal: A GlobalPerspective, vol. 25, no. 1-3, pp. 18-31, 2016.
  21. M. M. Rathore, A. Paul, A. Ahmad, N. Chilarnkurti, W.-H. Hong, and H. Seo, "Real-time secure communication for smart city in high-speed big data environment," Future Generation Computer Systems, vol. 83, pp. 638-652, 2018.
  22. J. Zhang, M. Zulkernine, and A. Haque, "Random-forests-based network intrusion detection systems," IEEE Transactions on Systems, Man, andCybernetics, Part C (Applications and Reviews), vol. 38, no. 5, pp. 649-659, 2008.
  23. K. Angrishi, "Turning internet of things (iot) into internet of vulnerabilities (iov): lot botnets," arXiv preprint arXiv:1702.03681, 2017.
  24. Alqazzaz, I. Alrashdi, E. Aloufi, M. Zohdy, and H. Ming, "Secsps: A secure and privacy-preserving framework for smart parking systems," Journal of Information Security, vol. 9, no. 04, pp. 299-314, 2018.
  25. A. V. Deorankar and S. S. Thakare, "Classification of Network Cyberattacks for Efficient Cognitive Fog Computing," 2020 Recent Innovations in Wireless Network Security Volume 2 Issue 1, HBRP Publication Page 1-7 2020, DOI: http://doi.org/10.5281/zenodo.3767885
  26. A. V. Deorankar and S. S. Thakare, "Survey on Anomaly Detection of (IoT)- Internet of Things Cyberattacks Using Machine Learning," 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2020, pp. 115-117, doi: 10.1109/ICCMC48092.2020.ICCMC-00023.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-08-30

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. A. V. Deorankar, Shiwani S. Thakare, " Efficient Cognitive Fog Computing for Classification of Network Cyberattacks Using Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.176-184, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206444