Abstract: In recent years, cybersecurity threats have grown exponentially due to the increasing interconnection of digital systems. Traditional intrusion detection systems (IDS) rely on predefined signatures and often fail to detect novel or evolving attacks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the accuracy and adaptability of IDS. This paper explores the integration of AI techniques—such as machine learning (ML), deep learning (DL), and neural networks—into intrusion detection frameworks. The proposed system aims to detect, classify, and prevent cyberattacks in real time. The results demonstrate that AI-based IDS provide better accuracy, reduced false alarm rates, and improved detection of zero-day attacks compared to traditional methods.

Keywords: Artificial Intelligence (AI), Cybersecurity, Intrusion Detection System (IDS), Machine Learning, Deep Learning, Network Security.

Downloads: | DOI: 10.17148/IARJSET.2025.121106