Enhancing Intrusion Detection Systems with a Hybrid Deep Learning Model and Optimized Feature Composition

Main Article Content

Dilip Motwani, Vidya Chitre, Varsha Bhosale, Sonaali Borkar, D. K. Chitre


Systems for detecting intrusions (IDS) are essential for protecting network infrastructures from hostile activity. Advanced methods are required since traditional IDS techniques frequently fail to properly identify sophisticated and developing assaults. In this article, we suggest a novel method for improving IDS performance through the use of a hybrid deep learning model and feature composition optimization. RNN and CNN has strengths that the proposed hybrid deep learning model leverages to efficiently capture both spatial and temporal correlations in network traffic data. The model can extract useful features from unprocessed network packets using CNNs and RNNs, giving a thorough picture of network behaviour. To increase the IDS's ability to discriminate, we also offer feature optimization strategies. We uncover the most pertinent and instructive features that support precise intrusion detection through a methodical feature selection and engineering process. In order to reduce the computational load and improve the model's efficiency without compromising detection accuracy, we also use dimensionality reduction approaches. We carried out extensive experiments using a benchmark dataset that is frequently utilized in intrusion detection research to assess the suggested approach. The outcomes show that the hybrid deep learning model performs better than conventional IDS methods, obtaining noticeably greater detection rates and lower false positive rates. The performance of model is further improved by the optimized feature composition, which offers a more accurate depiction of network traffic patterns.

Article Details

How to Cite
Dilip Motwani, et al. (2023). Enhancing Intrusion Detection Systems with a Hybrid Deep Learning Model and Optimized Feature Composition. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 369–380. https://doi.org/10.17762/ijritcc.v11i10.8499
Author Biography

Dilip Motwani, Vidya Chitre, Varsha Bhosale, Sonaali Borkar, D. K. Chitre

Dr. Dilip Motwani1, Dr Vidya Chitre2, Dr Varsha Bhosale3, Sonaali Borkar4, D.K.Chitre5

1,2,3,4Professor, Vidyalankar institute of Technology, Wadala, Mumbai, Maharashtra, India

5Terna college of Engineering, Navi Mumbai, Maharashtra, India

dilip.motwani @vit.edu.in1, vidya.chitre@vit.edu.in2, varsha.bhosale@vit.edu.in3, sonaali.borkar@vit.edu.in4, dnyanobachitre@ternaengg.ac.in5


G. De Carvalho Bertoli et al., "An End-to-End Framework for Machine Learning-Based Network Intrusion Detection System," in IEEE Access, vol. 9, pp. 106790-106805, 2021, doi: 10.1109/ACCESS.2021.3101188.

Z. A. El Houda, B. Brik and S. -M. Senouci, "A Novel IoT-Based Explainable Deep Learning Framework for Intrusion Detection Systems," in IEEE Internet of Things Magazine, vol. 5, no. 2, pp. 20-23, June 2022, doi: 10.1109/IOTM.005.2200028.

A. Pandit, A. Gupta, M. Bhatia and S. C. Gupta, "Filter Based Feature Selection Anticipation of Automobile Price Prediction in Azure Machine Learning," 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), Faridabad, India, 2022, pp. 256-262, doi: 10.1109/COM-IT-CON54601.2022.9850615.

C. -M. Ou, "Host-based Intrusion Detection Systems Inspired by Machine Learning of Agent-Based Artificial Immune Systems," 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Sofia, Bulgaria, 2019, pp. 1-5, doi: 10.1109/INISTA.2019.8778269.

P. Widulinski and K. Wawryn, "Parameter Efficiency Testing for an Intrusion Detection System Inspired by the Human Immune System," 2022 29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), Wroc?aw, Poland, 2022, pp. 208-212, doi: 10.23919/MIXDES55591.2022.9838210.

Khraisat, A.; Gondal, I.; Vamplew, P.; Kamruzzaman, J., “Survey of intrusion detection systems: Techniques, datasets and challenges”, Cybersecurity 2019, 2, 20

D. Dal, S. Abraham, A. Abraham, S. Sanyal and M. Sanglikar, "Evolution Induced Secondary Immunity: An Artificial Immune System Based Intrusion Detection System," 2008 7th Computer Information Systems and Industrial Management Applications, Ostrava, Czech Republic, 2008, pp. 65-70, doi: 10.1109/CISIM.2008.31.

V. Hnamte and J. Hussain, "An Extensive Survey on Intrusion Detection Systems: Datasets and Challenges for Modern Scenario," 2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), Kuala Lumpur, Malaysia, 2021, pp. 1-10, doi: 10.1109/ICECIE52348.2021.9664737.

J. Zhang, M. Zulkernine and A. Haque, "Random-Forests-Based Network Intrusion Detection Systems," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 5, pp. 649-659, Sept. 2008, doi: 10.1109/TSMCC.2008.923876.

Kwon, D.; Kim, H.; Kim, J.; Suh, S.C.; Kim, I.; Kim, K.J. A survey of deep learning-based network anomaly detection. Clust. Comput. 2017, 22, 949–961.

W. Cao, H. Zhang, W. He, H. Chen and E. H. Tat, "Autoencoder in Autoencoder Network Based on Low-Rank Embedding for Anomaly Detection in Hyperspectral Images," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 3263-3266, doi: 10.1109/IGARSS46834.2022.9884142.

J. Lansky et al., "Deep Learning-Based Intrusion Detection Systems: A Systematic Review," in IEEE Access, vol. 9, pp. 101574-101599, 2021, doi: 10.1109/ACCESS.2021.3097247.

X. Wang, L. Wang and Q. Wang, "Local Spatial–Spectral Information-Integrated Semisupervised Two-Stream Network for Hyperspectral Anomaly Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5535515, doi: 10.1109/TGRS.2022.3196409.

Meryem, A.; Ouahidi, B.E.L. Hybrid intrusion detection system using machine learning. Netw. Secur. 2020, 2020, 8–19.

S. A. Bajpai and A. B. Patankar, "A Study on Self-Configuring Intrusion Detection Model based on Hybridized Deep Learning Models," 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 303-309, doi: 10.1109/ICCMC56507.2023.10084290.

Abrar, I.; Ayub, Z.; Masoodi, F.; Bamhdi, A.M. A machine learning approach for intrusion detection system on NSL-KDD dataset. In Proceedings of the 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 10–12 September 2020.

Alzahrani, A.O.; Alenazi, M.J. Designing a network intrusion detection system based on machine learning for software defined networks. Future Internet 2021, 13, 111.

Disha, R.A.; Waheed, S. Performance analysis of machine learning models for intrusion detection system using Gini impurity-based weighted random forest (GIWRF) feature selection technique. Cybersecurity 2022, 5, 1.

Megantara, A.A.; Ahmad, T. A hybrid machine learning method for increasing the performance of Network Intrusion Detection Systems. J. Big Data 2021, 8, 142.

Ho, S.; Jufout SAl Dajani, K.; Mozumdar, M. A Novel Intrusion Detection Model for Detecting Known and Innovative Cyberattacks Using Convolutional Neural Network. IEEE Open J Comput Soc. 2021, 2, 14–25.

Priyanka, V.; Gireesh Kumar, T. Performance Assessment of IDS Based on CICIDS-2017 Dataset. In Information and Communication Technology for Competitive Strategies (ICTCS 2020); Lecture Notes in Networks and Systems; Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V., Eds.; Springer: Singapore, 2022; Volume 191.

Sun, P.; Liu, P.; Li, Q.; Liu, C.; Lu, X.; Hao, R.; Chen, J. DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system. Secur. Commun Netw. 2020, 2020, 8890306.

Mauro, M.D.; Galatro, G.; Liotta, A. Experimental Review of Neural-based approaches for network intrusion management. IEEE Trans. Netw. Serv. Manag. 2020, 17, 2480–2495.

Dong, S.; Xia, Y.; Peng, T. Network abnormal traffic detection model based on semi-supervised Deep Reinforcement Learning. IEEE Trans. Netw. Serv. Manag. 2021, 18, 4197–4212.

Pelletier, C.; Webb, G.I.; Petitjean, F. Deep learning for the classification of sentinel-2 Image time series. In Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019.

Lee, J.; Pak, J.G.; Lee, M. Network intrusion detection system using feature extraction based on deep sparse autoencoder. In Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea, 21–23 October 2020.