Thesis Abstract

Abstract
Cybersecurity has become a critical concern in the modern digital age, with the increasing sophistication of cyber threats posing significant risks to individuals, organizations, and nations. Anomaly detection plays a crucial role in identifying and mitigating these threats by identifying deviations from normal behavior within a system. This thesis explores the application of machine learning algorithms to enhance anomaly detection capabilities in cybersecurity. The study focuses on developing and evaluating machine learning models for anomaly detection, leveraging the power of artificial intelligence to improve the accuracy and efficiency of threat detection. The research begins with a comprehensive review of the existing literature on anomaly detection, machine learning algorithms, and cybersecurity. This literature review provides a solid foundation for understanding the current state-of-the-art techniques and identifying gaps in the research that can be addressed through this study. The methodology chapter outlines the research design, data collection methods, and the implementation of machine learning algorithms for anomaly detection. The empirical findings chapter presents the results of experiments conducted to evaluate the performance of different machine learning algorithms in detecting anomalies in cybersecurity datasets. The discussion of findings delves into the strengths and limitations of each algorithm, highlighting their effectiveness in identifying various types of cyber threats. The study also explores the implications of these findings for enhancing cybersecurity practices and strategies. In conclusion, this thesis underscores the significance of leveraging machine learning algorithms for anomaly detection in cybersecurity. By harnessing the power of artificial intelligence, organizations can bolster their defenses against evolving cyber threats and improve their overall security posture. The insights gained from this research contribute to the advancement of anomaly detection techniques and offer practical recommendations for implementing machine learning solutions in real-world cybersecurity scenarios. Ultimately, this study aims to pave the way for more robust and effective cybersecurity measures in the face of an increasingly complex threat landscape.

Thesis Overview