Intrusion Detection System Using Gated Recurrent Neural Network

Volume: 10 | Issue: 01 | Year 2024 | Subscription
International Journal of Software Computing and Testing
Received Date: 03/28/2024
Acceptance Date: 04/19/2024
Published On: 2024-05-29
First Page: 1
Last Page: 8

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By: Purushotam Naidu K., S. Deepika, N. Sri Ramya, N. Sharmila, K. Vaishnavi, and P. Bhavitha

Abstract

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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Citation:

How to cite this article: Purushotam Naidu K., S. Deepika, N. Sri Ramya, N. Sharmila, K. Vaishnavi, and P. Bhavitha, Intrusion Detection System Using Gated Recurrent Neural Network. International Journal of Software Computing and Testing. 2024; 10(01): 1-8p.

How to cite this URL: Purushotam Naidu K., S. Deepika, N. Sri Ramya, N. Sharmila, K. Vaishnavi, and P. Bhavitha, Intrusion Detection System Using Gated Recurrent Neural Network. International Journal of Software Computing and Testing. 2024; 10(01): 1-8p. Available from:https://journalspub.com/publication/ijsct-v10i01-6704/
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