One of my most impactful contributions has been my research on keylogger detection using machine learning algorithms and the Artificial Immune System Algorithm, which addresses the critical issue of data breaches and cybersecurity threats. This work provides a robust solution for detecting malicious keyloggers, a major cause of sensitive information leaks, by leveraging machine learning models to enhance detection accuracy. By incorporating the Artificial Immune System Algorithm, the model mimics biological immune responses to identify and counteract malicious activities effectively. This innovative approach not only improves detection rates but also reduces false positives, offering a practical and reliable solution for organizations to safeguard user data. Published in a Scopus-indexed journal, this research has significant industry implications, particularly in enhancing security for financial systems, enterprises, and personal computing environments. It reflects my dedication to advancing cybersecurity solutions and my ability to translate theoretical knowledge into practical, impactful innovations.
Link to the published Research Paper in the Scopus-Indexed Journal: https://www.iieta.org/journals/ria/paper/10.18280/ria.380128