Skip to main content
SpringerLink
Log in

Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network

  • Conference paper
  • First Online:
Computational Intelligence in Information Systems (CIIS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 888))

Abstract

Botnet represent a critical threat to computer networks because their behavior allows hackers to take control of many computers simultaneously. Botnets take over the device of their victim and performs malicious activities on its system. Although many solutions have been developed to address the detection of Botnet in real time, these solutions are still prone to several problems that may critically affect the efficiency and capability of identifying and preventing Botnet attacks. The current work proposes a technique to detect Botnet attacks using a feed-forward backpropagation artificial neural network. The proposed technique aims to detect Botnet zero-day attack in real time. This technique applies a backpropagation algorithm to the CTU-13 dataset to train and evaluate the Botnet detection classifier. It is implemented and tested in various neural network designs with different hidden layers. Results demonstrate that the proposed technique is promising in terms of accuracy and efficiency of Botnet detection.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shah, S., Jani, H., Shetty, S., Bhowmick, K.: Virus detection using artificial neural networks. Int. J. Comput. Appl. 84(5), 0975–8887 (2013)

    Google Scholar 

  2. Ahmed, A.A.: Investigation model for DDoS attack detection in real-time. Int. J. Soft. Eng. Comput. Syst. 1(1), 93–105 (2015)

    Article  Google Scholar 

  3. Ahmed, A.A., Jantan, A., Wan, T.-C.: Real-time detection of intrusive traffic in QoS network domains. IEEE Secur. Priv. 11(6), 45–53 (2013)

    Article  Google Scholar 

  4. Ahmed, A.A., Jantan, A., Wan, T.-C.: Filtration model for the detection of malicious traffic in large-scale networks. Comput. Commun. 82, 59–70 (2016)

    Article  Google Scholar 

  5. Ahmed, A.A., Jantan, A., Rasmi, M.: Service violation monitoring model for detecting and tracing bandwidth abuse. J. Netw. Syst. Manage. 21(2), 218–237 (2013)

    Article  Google Scholar 

  6. Narang, P., Ray, S., Hota, C., Venkatakrishnan, V.: Peershark: detecting peer-to-peer botnets by tracking conversations. In: Security and Privacy Workshops (SPW), IEEE, pp. 108–115 (2014)

    Google Scholar 

  7. Dai, Q., Zhang, C., Wu, H.: Research of decision tree classification algorithm in data mining. Int. J. Database Theory Appl. 9(5), 1–8 (2016)

    Article  Google Scholar 

  8. Kalaivani, P., Vijaya, M.S.: Mining based detection of botnet traffic in network flow. IRACST—Int. J. Comput. Sci. Inf. Technol. Secur. (IJCSITS) 6(1), 2249–9555 (2016)

    Google Scholar 

  9. Jantan, A., Ahmed, A.A.: Honeybee protection system for detecting and preventing network attacks. J. Theor. Appl. Inf. Technol. 64(1), 38–47 (2014)

    Google Scholar 

  10. Jantan, A., Ahmed, A.A.: Honey bee intelligent model for network zero day attack detection. Int. J. Digit. Content Technol. Appl. 8(6), 45–52 (2014)

    Google Scholar 

  11. Singh, k, Guntuku, S.C., Thakur, A., Hota, C.: Big data analytics framework for peer-to-peer botnet detection using random forests. Inform. Sci. 278, 488–497 (2014)

    Article  Google Scholar 

  12. Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemometr. Intell. Lab. Syst. 39(1), 43–62 (1997)

    Article  Google Scholar 

  13. Garcia, S., Grill, M., Stiborek, J., Zunino, A.: An empirical comparison of botnet detection methods. Comput. Secur. 45, 100–123 (2014)

    Article  Google Scholar 

  14. Karasaridis, A., Rexroad, B., Hoeflin, D.A.: Wide-scale botnet detection and characterization. HotBots 7, 7 (2007)

    Google Scholar 

  15. Gu, G., Zhang, J., Lee, W.: BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic. In: NDSS, vol. 8, pp. 1–18 (2008)

    Google Scholar 

  16. Al-Duwairi, B., Al-Ebbini, L.: BotDigger: A fuzzy inference system for botnet detection. In: 2010 Fifth International Conference on Internet Monitoring and Protection (ICIMP), pp. 16–21. IEEE (2010)

    Google Scholar 

  17. Masud, M.M., Al-Khateeb, T., Khan, L., Thuraisingham, B., Hamlen, K.W.: Flow-based identification of botnet traffic by mining multiple log files. In: First International Conference on Distributed Framework and Applications, DFmA, pp. 200–206. IEEE (2008)

    Google Scholar 

  18. Rumelhart, D.E., Durbin, R., Golden, R., Chauvin, Y.: Backpropagation: the basic theory. In: Backpropagation: Theory, Architectures and Applications, pp. 1–34 (1995)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Faculty of Computer System and Software Engineering, Universiti Malaysia Pahang under FRGS Grant No. RDU160106 and RDU Grant No. RDU160365.

Author information

Authors and Affiliations

  1. Systems Network & Security (SysNetS) Research Group, Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, 26300, Kuantan, Malaysia

    Abdulghani Ali Ahmed

Authors
  1. Abdulghani Ali Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdulghani Ali Ahmed .

Editor information

Editors and Affiliations

  1. School of Computing and Informatics, Universiti Teknologi Brunei, Gadong, Brunei Darussalam

    Saiful Omar

  2. School of Computing and Informatics, Universiti Teknologi Brunei, Gadong, Brunei Darussalam

    Wida Susanty Haji Suhaili

  3. Centre for Innovative Engineering, Universiti Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahmed, A.A. (2019). Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network. In: Omar, S., Haji Suhaili, W., Phon-Amnuaisuk, S. (eds) Computational Intelligence in Information Systems. CIIS 2018. Advances in Intelligent Systems and Computing, vol 888. Springer, Cham. https://doi.org/10.1007/978-3-030-03302-6_3

Download citation

Publish with us

Policies and ethics

Search

Navigation