Contribution to Threat Management Through the Use of AI-Based IDS

Abstract

Objectives: This paper aims to enhance cybersecurity through the integration of Artificial Intelligence (AI) in Intrusion Detection Systems (IDS), addressing the limitations of traditional IDS in detecting evolving cyber threats.   Theoretical Framework: The study builds on existing research in cybersecurity, focusing on AI techniques such as decision trees and linear regression to improve the accuracy and effectiveness of AI-based IDS.   Method: A comprehensive review of current AI-IDS methodologies is conducted, alongside an exploration of machine learning algorithms applied to datasets like KDD99 and NSL-KDD. The proposed architecture utilizes supervised machine learning to predict anomalies in network traffic.   Results and Discussion: The findings indicate that AI-IDS can significantly reduce false positives and enhance detection of zero-day attacks through adaptive learning. The results highlight the importance of quality data and continuous model refinement.   Research Implications: This research underscores the necessity for ongoing exploration of AI techniques in cybersecurity, suggesting future studies focus on real-time adaptive systems to further improve threat detection.   Originality/Value: This paper contributes to the field by providing insights into the practical application of AI in IDS, offering a structured approach that combines theoretical knowledge with empirical evidence, thus paving the way for future innovations in cybersecurity.