Open Access
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
Article Number 03003
Number of page(s) 5
Section Computing
Published online 29 July 2020
  1. Zhang, Yong, et al. “Network intrusion detection: Based on deep hierarchical network and original flow data.” IEEE Access 7 (2019): 37004-37016. [Google Scholar]
  2. Wang, Meng, Yiqin Lu, and Jiancheng Qin. “A dynamic MLP-based DDoS attack detection method using feature selection and feedback.” Computers & Security 88 (2020): 101645. [Google Scholar]
  3. Yi, Y.A., and Myat Myat Min. “An analysis of random forest algorithm based network intrusion detection system.” Proceedings of the International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Kanazawa, Japan. 2017. [Google Scholar]
  4. Barapatre, Prachi, et al. “Training MLP neural network to reduce false alerts in IDS.” 2008 International Conference on Computing, Communication and Networking. IEEE, 2008. [Google Scholar]
  5. Chamou, Dimitra, et al. “Intrusion Detection System Based on Network Traffic Using Deep Neural Networks.” 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2019. [Google Scholar]
  6. Vinayakumar, R., K.P. Soman, and Prabaharan Poornachandran. “Applying convolutional neural network for network intrusion detection.” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017. [Google Scholar]
  7. Ertam, Fatih, and Orhan Yaman. “Intrusion detection in computer networks via machine learning algorithms.” 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. [Google Scholar]
  8. Esmaily, Jamal, Reza Moradinezhad, and Jamal Ghasemi. “Intrusion detection system based on multi-layer perceptron neural networks and decision tree.” 2015 7th Conference on Information and Knowledge Technology (IKT). IEEE, 2015. [Google Scholar]
  9. Ahmad, Iftikhar, et al. “Intrusion detection using feature subset selection based on MLP.” Sci Res Essays 6.34 (2011): 6804-6810. [Google Scholar]
  10. Yulianto, Arif, Parman Sukarno, and Novian Anggis Suwastika. “Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset.” Journal of Physics: Conference Series. Vol. 1192. No. 1. IOP Publishing, 2019. [Google Scholar]
  11. Sathesh, A. “ENHANCED SOFT COMPUTING APPROACHES FOR INTRUSION DETECTION SCHEMES IN SOCIAL MEDIA NETWORKS.” Journal of Soft Computing Paradigm (JSCP) 1.02 (2019): 69-79. [Google Scholar]
  12. Anguraj, Dinesh Kumar, and S. Smys. “Trust-based intrusion detection and clustering approach for wireless body area networks.” Wireless Personal Communications 104.1 (2019): 1-20. [Google Scholar]
  13. Dhanabal, L., and S.P. Shantharajah. “A study on NSL-KDD dataset for intrusion detection system based on classification algorithms.” International Journal of Advanced Research in Computer and Communication Engineering 4.6 (2015): 446-452. [Google Scholar]
  14. Shone, Nathan, et al. “A deep learning approach to network intrusion detection.” IEEE Transactions on Emerging Topics in Computational Intelligence 2.1 (2018): 41-50. [Google Scholar]
  15. CIC dataset : [Google Scholar]

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