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All Projects → vinayakumarr → Network Intrusion Detection

vinayakumarr / Network Intrusion Detection

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

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python
139335 projects - #7 most used programming language

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deep-learningmachine-learningcybersecurityintrusion-detectionnetwork-security

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Network-Intrusion-Detection

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

DOI

KDDCup '99': http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

NSL-KDD: http://www.unb.ca/cic/datasets/nsl.html

UNSW-NB15: https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/

Please cite the following papers, if you use the code as part of your research

  1. R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran: Applying convolutional neural network for network intrusion detection. ICACCI 2017: 1222-1228 https://ieeexplore.ieee.org/abstract/document/8126009

  2. R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran: Evaluating effectiveness of shallow and deep networks to intrusion detection system. ICACCI 2017: 1282-1289 https://ieeexplore.ieee.org/document/8126018

  3. R. Vinayakumar, K. P. Soman, Prabaharan Poornachandran: Evaluation of Recurrent Neural Network and its variants for Intrusion Detection System (IDS)" has accepted in Special Issue On Big Data Searching, Mining, Optimization & Securing (BSMOS) Peer to Peer Cloud Based Networks in IJISMD https://www.igi-global.com/article/evaluation-of-recurrent-neural-network-and-its-variants-for-intrusion-detection-system-ids/204371

  4. Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access, 7, 41525-41550. https://ieeexplore.ieee.org/abstract/document/8681044

  5. Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2019). A Comparative Analysis of Deep Learning Approaches for Network Intrusion Detection Systems (N-IDSs): Deep Learning for N-IDSs. International Journal of Digital Crime and Forensics (IJDCF), 11(3), 65-89. https://www.igi-global.com/article/a-comparative-analysis-of-deep-learning-approaches-for-network-intrusion-detection-systems-n-idss/227640

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