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Recent Trends in Electronics Communication Systems Original Research

Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques

  1. Sayed Abdulhayan P A College of Engineering, Mangalore,
  2. Varsha M P A College of Engineering, Mangalore,
  3. Adam Adhil P A College of Engineering, Mangalore,
  4. Hazil Muhammed P A College of Engineering, Mangalore,
  5. Mohamed Favas V.P. P A College of Engineering, Mangalore,
  6. Mohammad Fadil P A College of Engineering, Mangalore,

Abstract

Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed the Difficult Set Sampling Technique (DSSTE). First, use the Edited Nearest Neighbor (ENN) approach to extract the easy and tough sets from the imbalanced training set. Next, use the KMeans technique to compress the majority samples in the difficult set to decrease the majority. Test both the more conventional NSL-KDD intrusion dataset and the more modern and comprehensive CSE-CIC-IDS2018 intrusion dataset. XGBoost, Support Vector Machine (SVM), Random Forest (RF), Short-and Long-Term Memory (LSTM), Mini-VGGNet, and AlexNet are examples of traditional classification models. The results of the experiment demonstrate that our proposed DSSTE algorithm performs better than the alternative.

Keywords

References (12)

  1. E. Denning, ‘‘An intrusion-detection model,’’ IEEE Trans. Softw. Eng., vol. SE13, no. 2, pp. 222–232, Feb. 1987.
  2. B. Amor, S. Benferhat, and Z. Elouedi, ‘‘Naive Bayes vs decision trees in intrusion detection systems,’’ in Proc. ACM Symp. Appl. Comput. (SAC), 2004, pp. 420–424
  3. Panda and M. R. Patra, ‘‘Network intrusion detection using Naive Bayes,’’ Int. J. Comput. Sci. Netw. Secur., vol. 7, no. 12, pp. 258–263, 2007
  4. A. M. Hasan, M. Nasser, B. Pal, and S. Ahmad, ‘‘Support vector machine and random forest modeling for intrusion detection system (IDS),’’ J. Intell. Learn. Syst. Appl., vol. 6, no. 1, pp. 45–52, 2014.
  5. Japkowicz, ‘‘The class imbalance problem: Significance and strategies,’’ in Proc. Int. Conf. Artif. Intell., vol. 56, 2000, pp. 111–117.
  6. LeCun, Y. Bengio, and G. Hinton, ‘‘Deep learning,’’ Nature, vol. 521, no. 7553, pp. 436–444, 2015
  7. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, ‘‘Deep learning for visual understanding: A review,’’ Neurocomputing, vol. 187, pp. 27–48, Apr. 2016
  8. Young, D. Hazarika, S. Poria, and E. Cambria, ‘‘Recent trends in deep learning based natural language processing [review article],’’ IEEE Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, Aug. 2018.
  9. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, ‘‘A deep learning approach to network intrusion detection,’’ IEEE Trans. Emerg. Topics Comput. Intell., vol. 2, no. 1, pp. 41–50, Feb. 2018.
  10. Bhattacharya, P. K. R. Maddikunta, R. Kaluri, S. Singh, T. R. Gadekallu, M. Alazab, and U. Tariq, ‘‘A novel PCA-firefly based XGBoost classification model for intrusion detection in networks using GPU,’’ Electronics, vol. 9, no. 2, p. 219, Jan. 2020.
  11. Javaid, Q. Niyaz, W. Sun, and M. Alam, ‘‘A deep learning approach for network intrusion detection system,’’ in Proc. 9th EAI Int. Conf. Bioinspired Inf. Commun. Technol. (Formerly BIONETICS), 2016, pp. 21–26.
  12. Kwon, H. Kim, J. Kim, S. C. Suh, I. Kim, and K. J. Kim, ‘‘A survey of deep learning-based network anomaly detection,’’ Cluster Comput., vol. 22, pp. 949– 961, 2019.
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