Enhancing Cybersecurity through Explainable AI and Interpretability Techniques

Thesis Abstract

Abstract

The field of cybersecurity faces increasing challenges due to the evolving nature of cyber threats and the complexity of modern IT environments. This research explores the potential of explainable AI and interpretability techniques in bolstering cybersecurity measures. The study begins with a comprehensive review of AI in cybersecurity, focusing on the significance of explainable AI, challenges in interpretability, and the current landscape of research in this domain. The methodology encompasses data collection, preprocessing, the selection and implementation of explainable AI models and algorithms, and the design of interpretable threat detection techniques. Performance evaluation metrics, ethical considerations, and experimentation setup are integral components of the research methodology. The implementation phase involves the deployment of explainable AI models, comparative analysis of interpretable cybersecurity AI techniques, and visualization of results. The study concludes with a summary of research findings, implications for cybersecurity advancements, future research directions, ethical considerations, and regulatory compliance in interpretable AI. This research provides insights into the potential of explainable AI to enhance cybersecurity, with implications for threat detection, user acceptance, and real-world applications.