In the realm of cybersecurity, the constant evolution of threats demands sophisticated intrusion detection systems (IDSs) capable of discerning intricate patterns in network traffic. This study proposes an IDS leveraging the capabilities of gated recurrent neural networks (GRNNs) to enhance the detection of anomalies and potential security breaches. The GRNN architecture, employing mechanisms like long short-term memory (LSTM) and gated recurrent unit (GRU), demonstrates efficacy in capturing long-range dependencies within sequential data, a critical attribute for analyzing network traffic. The proposed system undergoes a comprehensive process, including data collection, preprocessing, and training on a labeled dataset encompassing normal and malicious network behaviors. During the training phase, the GRNN refines its parameters to recognize patterns in network traffic. In the operational phase, the system continuously analyses incoming traffic, employing a predefined threshold to trigger alarms upon detecting potential intrusions. The benefits of employing GRNNs lie in their adaptability to changing traffic patterns and their capability to provide real-time intrusion detection. However, challenges include the need for substantial labeled training data and careful model optimization. The proposed IDS not only contributes to the arsenal of cybersecurity tools but also underscores the importance of leveraging advanced neural network architectures for effective and adaptive network security in the face of evolving threats.
Keywords: Neural networks, cybersecurity, GRU, IDS, real-time detection, XGBoost, GRNN, LSTM

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1. Jadhav KP, Arjariya T, Gangwar M. Hybrid-Ids: an approach for intrusion detection system with hybrid feature extraction technique using supervised machine learning. Int J Intell Syst Appl Eng. 2023;11:591-597.
2. Jadhav KP, Arjariya T, Gangwar M. Intrusion detection system using recurrent neural network-long short-term memory. Int J Intell Syst Appl Eng. 2023;11:563-573.
3. Udas PB, Karim ME, Roy KS. SPIDER: A shallow PCA based network intrusion detection system with enhanced recurrent neural networks. J King Saud Univ Comput Inf Sci. 2022;34:10246-10272. DOI: 10.1016/j.jksuci.2022.10.019.
4. Aldallal A. Toward efficient intrusion detection system using hybrid deep learning approach. Symmetry. 2022;14(9):1916. DOI: 10.3390/sym14091916.
5. Gautam S, Henry A, Zuhair M, Rashid M, Javed AR, Maddikunta PK. A composite approach of intrusion detection systems: hybrid RNN and correlation-based feature optimization. Electronics. 2022;11:3529. DOI: 10.3390/electronics11213529.
6. Yin C, Zhu Y, Fei J, He X. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access. 2017;5:21954-21961. DOI: 10.1109/ACCESS.2017.2762418.
7. Almasoudy FH, Al-Yaseen WL, Idrees AK. Differential evolution wrapper feature selection for intrusion detection system. Procedia Comput Sci. 2020;167:1230-1239. DOI: 10.1016/j.procs.03.438.
8. Ge Y, Li J, Tian Y. Internet of Things Intrusion Detection System Based on D-GRU. 2022 4th International Conference on Applied Machine Learning (ICAML); 2022 Jun 17-19; Changsha, China. IEEE; 2022. p. 1-6. doi:10.1109/ICAML57167.2022.00066.
9. Zhang M, Fernández-Torres MÁ, Camps-Valls G. Hybrid Recurrent Neural Network for Drought Monitoring. NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning; 2022. Available from: https://www.climatechange.ai/papers/neurips2022/51.
10. Bingu R, Jothilakshmi S. Design of Intrusion Detection System using Ensemble Learning Technique in Cloud Computing Environment. Int J Adv Comput Sci Appl (IJACSA). 2023;14(5). doi: 10.14569/IJACSA.2023.0140580.
11. Xu C, Shen J, Du X, Zhang F. An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access. 2018;6:48697-48707. doi: 10.1109/ACCESS.2018.2867564.
12. Roy KS, Ahmed T, Udas PB, Karim ME, Majumdar S. Malhystack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis. Intell Syst Appl. 2023;20:200283. DOI: 10.1016/j.iswa.2023.200283.
13. Gunjal SP, Aher SM. Network intrusion detection using recurrent neural network algorithm. Int Res J Eng Technol. 2020;07:2143-2148.
14. Kasongo SM. A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Comput Commun. 2023;199:113-125. DOI: 10.1016/j.comcom.2022.010.

In the realm of cybersecurity, the constant evolution of threats demands sophisticated intrusion detection systems (IDSs) capable of discerning intricate patterns in network traffic. This study proposes an IDS leveraging the capabilities of gated recurrent neural networks (GRNNs) to enhance the detection of anomalies and potential security breaches. The GRNN architecture, employing mechanisms like long short-term memory (LSTM) and gated recurrent unit (GRU), demonstrates efficacy in capturing long-range dependencies within sequential data, a critical attribute for analyzing network traffic. The proposed system undergoes a comprehensive process, including data collection, preprocessing, and training on a labeled dataset encompassing normal and malicious network behaviors. During the training phase, the GRNN refines its parameters to recognize patterns in network traffic. In the operational phase, the system continuously analyses incoming traffic, employing a predefined threshold to trigger alarms upon detecting potential intrusions. The benefits of employing GRNNs lie in their adaptability to changing traffic patterns and their capability to provide real-time intrusion detection. However, challenges include the need for substantial labeled training data and careful model optimization. The proposed IDS not only contributes to the arsenal of cybersecurity tools but also underscores the importance of leveraging advanced neural network architectures for effective and adaptive network security in the face of evolving threats.
Keywords: Neural networks, cybersecurity, GRU, IDS, real-time detection, XGBoost, GRNN, LSTM

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Jadhav KP, Arjariya T, Gangwar M. Hybrid-Ids: an approach for intrusion detection system with hybrid feature extraction technique using supervised machine learning. Int J Intell Syst Appl Eng. 2023;11:591-597.
2. Jadhav KP, Arjariya T, Gangwar M. Intrusion detection system using recurrent neural network-long short-term memory. Int J Intell Syst Appl Eng. 2023;11:563-573.
3. Udas PB, Karim ME, Roy KS. SPIDER: A shallow PCA based network intrusion detection system with enhanced recurrent neural networks. J King Saud Univ Comput Inf Sci. 2022;34:10246-10272. DOI: 10.1016/j.jksuci.2022.10.019.
4. Aldallal A. Toward efficient intrusion detection system using hybrid deep learning approach. Symmetry. 2022;14(9):1916. DOI: 10.3390/sym14091916.
5. Gautam S, Henry A, Zuhair M, Rashid M, Javed AR, Maddikunta PK. A composite approach of intrusion detection systems: hybrid RNN and correlation-based feature optimization. Electronics. 2022;11:3529. DOI: 10.3390/electronics11213529.
6. Yin C, Zhu Y, Fei J, He X. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access. 2017;5:21954-21961. DOI: 10.1109/ACCESS.2017.2762418.
7. Almasoudy FH, Al-Yaseen WL, Idrees AK. Differential evolution wrapper feature selection for intrusion detection system. Procedia Comput Sci. 2020;167:1230-1239. DOI: 10.1016/j.procs.03.438.
8. Ge Y, Li J, Tian Y. Internet of Things Intrusion Detection System Based on D-GRU. 2022 4th International Conference on Applied Machine Learning (ICAML); 2022 Jun 17-19; Changsha, China. IEEE; 2022. p. 1-6. doi:10.1109/ICAML57167.2022.00066.
9. Zhang M, Fernández-Torres MÁ, Camps-Valls G. Hybrid Recurrent Neural Network for Drought Monitoring. NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning; 2022. Available from: https://www.climatechange.ai/papers/neurips2022/51.
10. Bingu R, Jothilakshmi S. Design of Intrusion Detection System using Ensemble Learning Technique in Cloud Computing Environment. Int J Adv Comput Sci Appl (IJACSA). 2023;14(5). doi: 10.14569/IJACSA.2023.0140580.
11. Xu C, Shen J, Du X, Zhang F. An intrusion detection system using a deep neural network with gated recurrent units. IEEE Access. 2018;6:48697-48707. doi: 10.1109/ACCESS.2018.2867564.
12. Roy KS, Ahmed T, Udas PB, Karim ME, Majumdar S. Malhystack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis. Intell Syst Appl. 2023;20:200283. DOI: 10.1016/j.iswa.2023.200283.
13. Gunjal SP, Aher SM. Network intrusion detection using recurrent neural network algorithm. Int Res J Eng Technol. 2020;07:2143-2148.
14. Kasongo SM. A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Comput Commun. 2023;199:113-125. DOI: 10.1016/j.comcom.2022.010.