Published Paper Details:

The rapid evolution of information technology has led to an increase in cyber threats, making network security a paramount concern for organizations worldwide. Network Intrusion Detection Systems (NIDS) play a crucial role in safeguarding networks by identifying and mitigating potential intrusions. Traditional rule-based approaches, while effective to some extent, face challenges in handling complex and evolving attack patterns. In response, the integration of Artificial Intelligence (AI) techniques into NIDS has garnered significant attention due to their ability to adapt to dynamic threats. This paper provides a comprehensive review and analysis of various AI-based approaches employed in NIDS, including machine learning, deep learning, and hybrid techniques. It explores the strengths and limitations of these methodologies, examines their performance in detecting different types of network attacks, and discusses current research trends and challenges. Furthermore, this study discusses the importance of dataset selection, feature engineering, model architecture, and evaluation metrics in the development and assessment of AI-driven NIDS. Through this analysis, insights are provided into the effectiveness of AI in enhancing the accuracy, scalability, and resilience of network intrusion detection systems, ultimately contributing to the advancement of cyber security.