The ever-evolving landscape of cyber threats demands continuous advancements in the security measures employed to safeguard critical internet infrastructure. This research explores the realm of Domain Name System (DNS) security, focusing on the detection of threats and vulnerabilities through comprehensive traffic analysis. The study investigates patterns, anomalies, and trends within DNS traffic to identify and mitigate potential threats in real time. Through the integration of machine learning algorithms and anomaly detection mechanisms, the system aims to provide a proactive defense against emerging cyber threats targeting the DNS infrastructure. The research also explores the integration of threat intelligence feeds to enhance the system’s ability to recognize and respond to the latest cyber threats.

Authors: O. Y. Ogunlola, A.S. Fakokunde, A. O. Oronti, O. O. Abereowo, O. D. Alowolodu, B. K. Alese

Published in: International Conference on Information Society (i-Society-2024)

• Date of Conference: 26-28 August, 2024
• DOI: 10.20533/iSociety.2024.0012
• ISBN: 978-1-913572-72-3
• Conference Location: Churchill College, Cambridge, UK