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Long Short-Term Memory-Based Recurrent Neural Network Approach for Intrusion Detection

  • Nishanth Rajkumar
  • Austen D’Souza
  • Sagaya Alex
  • G. Jaspher W. KathrineEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Intrusion detection is very essential in the field of information security. The cornerstone of an Intrusion Detection System (IDS) is to accurately identify different attacks in a network. In this paper, a deep learning system to detect intrusions is proposed. The existing recurrent neural network (RNN-IDS) based IDS is expanded to include Long Short term memory (LSTM) and the results are compared. The binary classification performance of the RNN-IDS is tested with various learning rates and using different number of hidden nodes. The results show that by integrating LSTM with RNN-IDS, the accuracy of intrusion prediction has improved against the benchmark dataset.

Keywords

Intrusion detection Recurrent neural network Long short term memory Deep learning 

References

  1. 1.
    Nadiammai GV, Hemalatha M (2014) Effective approach toward intrusion detection system using data mining techniques. Egypt Inf J 15(1):37–50CrossRefGoogle Scholar
  2. 2.
    Sabri FNM, Norita NM, Seman K (2011) Identifying false alarm rates for intrusion detection system with data mining. Int J Comput Sci Netw Secur 11:4Google Scholar
  3. 3.
    Dewa Z, Maglaras LA (2016) Data mining and intrusion detection systems data mining and intrusion detection systems. (IJACSA) Int J Adv Comput Sci Appl 7(1)Google Scholar
  4. 4.
    Patel J, Panchal K (2015) Effective intrusion detection system using data mining technique. IJSRD—Int J Sci Res Dev 3(02)Google Scholar
  5. 5.
    Miller Z, Deitrick W, Wei H (2011) Anomalous network packet detection using data stream mining. J Inf Secur 2:158–168Google Scholar
  6. 6.
    Javaid A, Niyaz Q, Sun W, Alam M (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 21–26Google Scholar
  7. 7.
    Chuan-long Y, Yue-fei Z, Jin-long F, Xin-zheng H (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access,  https://doi.org/10.1109/access.2017.2762418, pp 21954–21961CrossRefGoogle Scholar
  8. 8.
    Shone N, Ngoc TN, Phai VD, Shi Q (2017) A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell 2(1):41–50CrossRefGoogle Scholar
  9. 9.
    Jabez J, Muthukumar B (2015) Intrusion detection system (IDS): anomaly detection using outlier detection approach. In: International conference on intelligent computing, communication & convergence (ICCC-2015), Procedia Computer Science, vol 48, pp 338–346CrossRefGoogle Scholar
  10. 10.
    Viegas EK, Santin AO, Oliveira LS (2017) Toward a reliable anomaly-based intrusion detection in real-world environments. Comput Netw 127:200–216CrossRefGoogle Scholar
  11. 11.
    Umer MF, Sher M, Bi Y (2017) Flow-based intrusion detection: techniques and challenges. Comput Secur 70:238–254CrossRefGoogle Scholar
  12. 12.
    Akashdeep IM, Kumar N (2017) A feature reduced intrusion detection system using ANN classifier. Expert Syst Appl 88:249–257CrossRefGoogle Scholar
  13. 13.
    Dias LP, Cerqueira JJF, Assis KDR, Almeida RC (2017) Using artificial neural network in intrusion detection systems to computer networks. In: Proceedings of computer science and electronic engineering (CEEC)Google Scholar
  14. 14.
    Wang H, Jie G, Wang S (2017) An effective intrusion detection framework based on SVM with feature augmentation. Knowl-Based Syst 136:130–139CrossRefGoogle Scholar
  15. 15.
    Jha J, Ragha L (2013) Intrusion detection system using support vector machine. Int J Appl Inf Syst (IJAIS) 3:25–30Google Scholar
  16. 16.
    Abd-Eldayem MM (2014) A proposed HTTP service based IDS. Egypt Inf J 15(1):13–24CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nishanth Rajkumar
    • 1
  • Austen D’Souza
    • 1
  • Sagaya Alex
    • 1
  • G. Jaspher W. Kathrine
    • 1
    Email author
  1. 1.Karunya Institute of Technology and SciencesCoimbatoreIndia

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