Skip to main content
SpringerLink
Log in

Analysis of intrusion detection in cyber attacks using DEEP learning neural networks

  • Original Paper
  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

In this digital period, internet has turned into an indispensable wellspring of correspondence in just about every calling. With the expanded use of system engineering, its security has developed to be exceptionally discriminating issue as the workstations in distinctive association hold very private data and touchy information. The system which helps in screening the system security is termed as Network detection. Intrusion detection is to get ambushes against a machine structure. One of the vital tests to Intrusion Detection is the issue of misjudgment, misdetection and unsuccessful deficiency of steady response to the strike. In the past years, as the second line of boundary after firewall, the Intrusion Detection (ID) strategy has got speedy progression. Two diverse Machine Learning techniques are prepared in this research work, which include both supervised and unsupervised, for Network Intrusion Detection. Naive Bayes (supervised learning) and Self Organizing Maps (unsupervised learning) are the presented techniques. Deep learning techniques such as CNN is used for feature extraction. These remain provisional chances adaptation technique and pointer variables transformation. The two machine learning procedures are prepared on both kind of transformed dataset and afterward their outcomes are looked at with respect to the correctness of intrusion detection. The best Detection Rate (DR) was for the 93.0% User to Root attack (U2R) attack type and the most horrible result was display for Denial of Service attack (DOS) attacks with 0.02%.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550. https://doi.org/10.1109/ACCESS.2019.2895334

    Article  Google Scholar 

  2. Liao H-J, Lin C-HR, Lin Y-C, Tung K-Y (2013) Intrusion detection system: a comprehensive review. J Netw Comput Appl 36(1):16–24

  3. Mallikarjunan, K Narasimha, K Muthupriya, and S Mercy Shalinie (2016). “A survey of distributed denial of service attack.” In 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–6. IEEE

  4. Criscuolo, Paul J (2000). Distributed denial of service: Trin00, tribe flood network, tribe flood network 2000, and stacheldraht ciac-2319. No. CIAC-2319. California Univ Livermore Radiation Lab

  5. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260

    Article  MathSciNet  Google Scholar 

  6. Berthold, MR, N Cebron, F Dill, TR Gabriel, T Kötter, T Meinl, P Ohl, C Sieb, K Thiel, and B Wiswedel (2007). “Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007).” : 319–326

  7. Zargar ST, Joshi J, Tipper D (2013) A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE communications surveys & tutorials 15(4):2046–2069

    Article  Google Scholar 

  8. Baras JS (2007) Security and trust for wireless autonomic networks systems and control methods. Eur J Control 13(2–3):105–133

    Article  Google Scholar 

  9. Javaid, Ahmad, Quamar Niyaz, Weiqing Sun, and Mansoor Alam 2016. “A deep learning approach for network intrusion detection system.” In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp. 21–26. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

  10. Davis JJ, Clark AJ (2011) Data preprocessing for anomaly based network intrusion detection: A review. Comput Secur 30(6–7):353–375

    Article  Google Scholar 

  11. Singh R, Kumar H, Singla RK (2015) An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications 42(22):8609–8624

    Article  Google Scholar 

  12. B Santhosh Kumar and SP Reddy Karri (2020). “Deep Learning Techniques for Implementation of Chatbots,” International conference on computer communication and informatics (ICCCI), Coimbatore, India, 2020, pp. 1–5, doi: https://doi.org/10.1109/ICCCI48352.2020.9104143

  13. Lai Y, Zhang J, Liu Z (2019) Industrial anomaly detection and attack classification method based on convolutional neural network. Security and Communication Networks 2019:1–11

  14. Song, Jungsuk, Hiroki Takakura, Yasuo Okabe, Masashi Eto, Daisuke Inoue, and Koji Nakao 2011. “Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation.” In Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, pp. 29–36. ACM

  15. M Anbarasan, Bala Anand Muthu, CB Sivaparthipan, Revathi Sundarasekar, Seifedine Kadry, Sujatha Krishnamoorthy, A Antony Dasel (2020). “Detection of flood disaster system based on IoT, big data and convolutional deep neural network”, Computer Communications, Elsevier, Volume 150, 15 January, Pages 150–157, DOI : https://doi.org/10.1016/j.comcom.2019.11.022

  16. Power R (1996) Current and future danger: a CSI primer on Computer Crime & Information Warfare. Computer Security Institute, San Francisco

    Google Scholar 

  17. Fan W, Miller M, Stolfo S, Lee W, Chan P (2004) Using artificial anomalies to detect unknown and known network intrusions. Knowl Inf Syst 6(5):507–527

  18. Pradhan M, Pradhan SK, Sahu SK (2012) Anomaly detection using artificial neural network. International Journal of Engineering Sciences & Emerging Technologies 2(1):29–36

  19. Spafford, Eugene H (1989) “Crisis and aftermath.” Commun ACM 32, no. 6

  20. Louvieris P, Clewley N, Liu X (2013) Effects-based feature identification for network intrusion detection. Neurocomputing 121:265–273

    Article  Google Scholar 

  21. Alhakami W, ALharbi A, Bourouis S, Alroobaea R, Bouguila N (2019) Network anomaly intrusion detection using a nonparametric Bayesian approach and feature selection. IEEE Access 7:52181–52190

  22. Farooqi, Ashfaq Hussain, and Farrukh Aslam Khan (2009). “Intrusion detection systems for wireless sensor networks: A survey.” In International Conference on Future Generation Communication and Networking, pp. 234–241. Springer, Berlin, Heidelberg

  23. Sallay H, Bourouis S (2015) Intrusion detection alert management for high-speed networks: current researches and applications. Security and Communication Networks 8(18):4362–4372

    Article  Google Scholar 

  24. Aldwairi M, Khamayseh Y, Al-Masri M (2015) Application of artificial bee colony for intrusion detection systems. Security and Communication Networks 8(16):2730–2740

  25. Tsai C-F, Hsu Y-F, Lin C-Y, Lin W-Y (2009) Intrusion detection by machine learning: A review. Expert systems with applications 36(10):11994–12000

    Article  Google Scholar 

  26. Dinesh JacksonSamuel R Fenil E Gunasekaran Manogaran Vivekananda GN Thanjaivadivel T Jeeva S Ahilan A (2019). “Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM” , Computer Networks, Elsevier, Volume 151, 14 March 2019, Pages 191–200, DOI: https://doi.org/10.1016/j.comnet.2019.01.028

  27. Potluri, Sasanka, and Christian Diedrich (2016). “Accelerated deep neural networks for enhanced Intrusion Detection System.” In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE

  28. Putchala, Manoj Kumar (2017). “Deep learning approach for intrusion detection system (ids) in the internet of things (iot) network using gated recurrent neural networks (gru)

  29. Syarif, Iwan, Adam Prugel-Bennett, and Gary Wills 2012. “Unsupervised clustering approach for network anomaly detection.” In International conference on networked digital technologies, pp. 135–145. Springer, Berlin, Heidelberg

  30. Feng, Yong, Zhong-Fu Wu, Kai-Gui Wu, Zhong-Yang Xiong, and Ying Zhou (2005). “An unsupervised anomaly intrusion detection algorithm based on swarm intelligence.” In 2005 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 3965–3969. IEEE

  31. Mohammadpour L, Ling TC, Liew CS, Chong CY A Convolutional Neural Network for Network Intrusion Detection System. Proceedings of the Asia-Pacific Advanced Network 46:50–55

  32. Wu SX, Banzhaf W (2010) The use of computational intelligence in intrusion detection systems: A review. Appl Soft Comput 10(1):1–35

  33. Scarfone, Karen, and Peter Mell 2012. Guide to intrusion detection and prevention systems (idps). No. NIST Special Publication (SP) 800–94 Rev. 1 (Draft). National Institute of Standards and Technology

  34. Litjens G, Kooi T, BabakEhteshamiBejnordi AAAS, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

Download references

Author information

Authors and Affiliations

  1. Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    Parasuraman Kumar

  2. Research Scholar, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    A. Anbarasa Kumar & A. Udayakumar

  3. Department of CSE, Kalaivanar N.S.K College of Engineering, Nagarcoil, Tamil Nadu, India

    C. Sahayakingsly

Authors
  1. Parasuraman Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Anbarasa Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Sahayakingsly
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Udayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Anbarasa Kumar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications

Guest Editor: Ching-Hsien Hsu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, P., Kumar, A.A., Sahayakingsly, C. et al. Analysis of intrusion detection in cyber attacks using DEEP learning neural networks. Peer-to-Peer Netw. Appl. 14, 2565–2584 (2021). https://doi.org/10.1007/s12083-020-00999-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00999-y

Keywords

Search

Navigation