Intrusion Detection System to Secure a Network using ACNN Model and Machine Learning
Ruchika Dungarani1, Satish Narayan Gujjar2

1Ruchika Dungarani, Research Scholar, Department of Computer Science & Engineering, University of Technology, Jaipur (R.J), India.

2Dr. Satish Narayan Gujjar, Professor, Department of Computer Science & Engineering, University of Technology, Jaipur (R.J), India.  

Manuscript received on 05 June 2024 | Revised Manuscript received on 13 June 2024 | Manuscript Accepted on 15 June 2024 | Manuscript published on 30 June 2024 | PP: 1-5 | Volume-12 Issue-6, June 2024 | Retrieval Number: 100.1/ijisme.G131912070724 | DOI: 10.35940/ijisme.G1319.12060624

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Abstract: As cyber threats continue to evolve in sophistication and diversity, the need for robust Intrusion Detection Systems (IDS) becomes paramount to safeguarding network integrity. This research explores the application of an innovative approach by integrating an Attention-based Convolutional Neural Network (ACNN) model with machine learning techniques to enhance the accuracy and efficiency of intrusion detection. The proposed system leverages the ACNN’s ability to capture contextual dependencies in network traffic data, enabling the extraction of intricate patterns indicative of potential intrusions. The ACNN’s attention mechanism focuses on relevant features within the data, improving the model’s discriminative power and adaptability to dynamic cyber threats. To achieve optimal performance, the ACNN is complemented with a machine learning framework that includes feature engineering, dimensionality reduction, and classification algorithms. This integrated approach allows the system to adapt and learn from evolving attack vectors, providing a proactive defense mechanism against both known and unknown threats. The research evaluates the proposed ACNN-based IDS using benchmark datasets and real-world network traffic scenarios. Comparative analysis against traditional IDS models showcases the superiority of the ACNN in terms of detection accuracy, false positive rates, and computational efficiency. Furthermore, the system’s adaptability to emerging threats is demonstrated through continuous learning and retraining mechanisms. Results indicate that the ACNN-based IDS not only exhibits superior performance but also demonstrates resilience against evasion techniques employed by malicious actors. The research findings contribute to the advancement of network security by presenting a cutting-edge solution that combines deep learning and machine learning for effective and adaptive intrusion detection.

Keywords: Intrusion Detection System, ACNN Model, Machine Learning, Network Security, Cyber Threats, Attention Mechanism, Deep Learning, Information Security
Scope of the Article: Machine Learning