CNN-based Network Intrusion Detection and Classification Model for Cyber-Attacks


Authors : Uwadia Anthony. O

Volume/Issue : Volume 9 - 2024, Issue 7 - July


Google Scholar : https://tinyurl.com/bddzykty

Scribd : https://tinyurl.com/46hmf33j

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1158

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : A Convolution Neural Network (CNN)-based Network Intrusion Detection Model for Cyber-attacks is of great value in identifying and classifying attacks on any network. The Knowledge Discovery in Database Cup '99 dataset containing approximately 4,900,000 single connection vectors was divided into two phases; 75% of the total dataset was used during the learning process of the machine learning technique, while 25% was used on a fully trained model to validate and evaluate its performance. The model's performance indicated that it can detect and classify different classes of attacks with an accuracy of 98% with 20 epochs at a 0.001 learning rate using machine learning. The model loss for the training and validation was 7.48% and 7.98%, respectively, over 20 epochs, which implies that the model performed better on the training dataset. This study demonstrated that the convolutional Neural network-based Network Intrusion Detection and classification model shows high detection and low false negative rates. The CNN model offers a high detection rate and fidelity to unknown attacks, i.e., it can differentiate between already-seen attacks and new zero-day attacks. At the end of the experiment, the proposed approach is suitable in modeling the network IDS for detecting intrusion attacks on computer networks thereby enabling a secured environment for the proper functioning of the system

Keywords : Component; Network; Intrusion Detection System (IDS); Convolutional Neural Network(CNN); Artificial Neural Network(ANN); Machine Learning (ML)

References :

  1. M. Almseidin, M. Alzubi, S. Kovacs, and M. Alkasassbeh, “Evaluation of machine learning algorithms for the intrusion detection system”, In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), 2017, 000277-000282
  2. I. Al-Turaiki, and N. Altwaijry, “A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection”,2021, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233218/
  3. A. Andalib, and V. Vakili Tabataba, “An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners”, 2020, https://arxiv.org/pdf/2001.11936
  4. B. Cao, C. Li, Y. Song, and X. Fan, “Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU”, 2022, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019421/
  5. L. D’hooge, M. Verkerken, T. Wauters, F. De Turck, and B. Volckaert, “Investigating Generalised Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection”, 2023, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9960990/
  6. M. Dima Genemo, “Suspicious activity recognition for monitoring cheating in exams”, 2022, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866922/
  7. M. Gao, L.. Ma, H. Liu, Z. Zhang, Z. Ning, and J. Xu, “ Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis”, 2020, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085765/
  8. A. Henry, S. Gautam, S. Khanna, K. Rabie, T. Shongwe, P. Bhattacharya, B. Sharma, and S. Chowdhury, “Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection Syste”, 2023, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866711/
  9. N. Ketkar, “Introduction to keras”, In Deep Learning with Python, Apress, Berkeley, CA, 2017, pp. 99-111.
  10. M. Kodys, Z. Lu, K. Wai Fok, and  V. L. Thing, “Intrusion Detection in Internet of Things using Convolutional Neural Networks”, 2022, https://arxiv.org/pdf/2211.10062
  11. A. Kumar Silivery, and R.  Mohan Rao Kovvur, “A model for multi-attack classification to improve intrusion detection performance using deep learning approaches”, 2023, https://arxiv.org/pdf/2310.16380
  12. A. Kumar Silivery, K. Ram Mohan Rao, and L. Suresh Kumar, “An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection” 2023, https://arxiv.org/pdf/2308.08803
  13. M. Mihailescu, D. Mihai, M. Carabas, M. Komisarek, M. Pawlicki, W. Hołubowicz, and R. Kozik, “The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset”, 2021, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272217/
  14. X. H. Nguyen, X. D. Nguyen, H. H. Huynh, and K. H. Le, “Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways” 2022, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778231/
  15. V. Ramanathan, K. Mahadevan, and S. Dua, “A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN) ”, 2023, https://arxiv.org/pdf/2309.05646
  16. M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of Network-Based Intrusion Detection Data Sets”,  Computers & Security, 2019, vol. 86, pp 147-167
  17. A. A. Sayed, A. A. Taher Azar, A. Ella Hassanien,  and S. El-Ola Hanafy, “Negative Selection Approach Application in Network Intrusion Detection Systems”, 2014, https://arxiv.org/pdf/1403.2716
  18. I. Shivhare, J. Purohit, V. Jogani, S. Attari, and D. Madhav Chandane, “Intrusion Detection: A Deep Learning Approach”, 2023, https://arxiv.org/pdf/2306.07601
  19. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set”, In 2009 IEEE symposium on
    computational intelligence for security and defense applications, 2009, pp. 1-6
  20. M. Vakili, M. Ghamsari, and M. Rezaei, “Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification”,  arXiv preprint arXiv:2001.09636, 2020, pp. 1-13.
  21. W. Wang, F. Harrou, B. Bouyeddou, S. M. Senouci, and Y. Sun, Y, “A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems”, 2022, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490 44/
  22. Z. Wang, F. A. Ghaleb, A. Zainal, M. Md Siraj, and X.  Lu, “An efficient intrusion detection model based on convolutional spiking neural network, 2024,  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10963367/
  23. P. Wu, H. Guo, and R. Buckland, “A Transfer Learning Approach for Network Intrusion Detection”,2019, https://arxiv.org/pdf/1909.02352
  24. T. Ahmad, D. Truscan, J. Vain, and I. Porres, “Early Detection of Network Attacks Using Deep Learning, 2022, https://arxiv.org/pdf/2201.11628
  25. O. Ceviz, P. Sadioglu, S. Sen, and V. G. Vassilakis, “A Novel Federated Learning-based Intrusion Detection System for Flying Ad Hoc Networks’,  2023, https://arxiv.org/pdf/2312.04135
  26. H. Dhillon and A. Haque, “Towards Network Traffic Monitoring Using Deep Transfer Learning”, 2021,  [ https://arxiv.org/pdf/2101.00731

A Convolution Neural Network (CNN)-based Network Intrusion Detection Model for Cyber-attacks is of great value in identifying and classifying attacks on any network. The Knowledge Discovery in Database Cup '99 dataset containing approximately 4,900,000 single connection vectors was divided into two phases; 75% of the total dataset was used during the learning process of the machine learning technique, while 25% was used on a fully trained model to validate and evaluate its performance. The model's performance indicated that it can detect and classify different classes of attacks with an accuracy of 98% with 20 epochs at a 0.001 learning rate using machine learning. The model loss for the training and validation was 7.48% and 7.98%, respectively, over 20 epochs, which implies that the model performed better on the training dataset. This study demonstrated that the convolutional Neural network-based Network Intrusion Detection and classification model shows high detection and low false negative rates. The CNN model offers a high detection rate and fidelity to unknown attacks, i.e., it can differentiate between already-seen attacks and new zero-day attacks. At the end of the experiment, the proposed approach is suitable in modeling the network IDS for detecting intrusion attacks on computer networks thereby enabling a secured environment for the proper functioning of the system

Keywords : Component; Network; Intrusion Detection System (IDS); Convolutional Neural Network(CNN); Artificial Neural Network(ANN); Machine Learning (ML)

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe