Design and Performance Evaluation of an AI-Based Intrusion Detection System Using Machine Learning Techniques

Utkarsha Akhepuriya and Jeetendra Singh Yadav

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 3, 99-107

Abstract: The rapid expansion of digital networks and online services has led to a significant increase in cyber threats, making Intrusion Detection Systems (IDS) an essential component of modern cybersecurity. Traditional IDS techniques, primarily based on signature matching, are often ineffective against evolving and unknown attacks. To overcome these limitations, the integration of Machine Learning has emerged as a powerful approach for intelligent and adaptive intrusion detection. This paper presents the design and performance evaluation of an AI-based intrusion detection system using various machine learning algorithms. The proposed system utilizes benchmark datasets such as NSL-KDD and CICIDS2017, along with preprocessing and feature selection techniques, to enhance detection capability. Experimental results demonstrate improved accuracy, detection rate, and F1-score, indicating the effectiveness of the model in identifying malicious activities. The study contributes to the development of a robust and scalable IDS framework capable of addressing modern cybersecurity challenges and provides valuable insights for future research in this domain.

Keywords: Intrusion Detection System (IDS); Machine Learning; Cybersecurity; Anomaly Detection; Performance Evaluation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i3:id:1577

DOI: 10.32628/IJSRST26133122

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