Authors - U.H.S. Rashmina Amarasinghe, K.A Dilini T. Kulawansa Abstract - This literature review examines the expanding and critical role of Artificial Intelligence, including Machine Learning and Deep Learning, in countering increasingly complex cyber threats. The purpose of this review is to analyze the applications, effectiveness, challenges, and future research directions of Artificial Intelligence driven technologies in threat detection. Artificial Intelligence driven systems significantly enhance the NIST Cybersecurity Framework functions (Identify, Protect, Detect, Respond, Recover). They excel at real time anomaly detection in massive datasets, outperforming traditional signature-based methods against modern attacks like zero-day exploits and polymorphic mal-ware. Key techniques discussed include Support Vector Machines, Decision Trees, and various Neural Networks used in effective Intrusion Detection Systems and phishing classification. However, the review highlights the dual nature of Artificial Intelligence, noting the rise of Artificial Intelligence driven cyberattacks and the challenges posed by high resource demands and managing data quality. Ethical considerations, specifically concerning privacy and transparency, necessitate the development of Explainable Artificial Intelligence. Ultimately, the future relies on Hybrid Augmented Intelligence, a strong human, Artificial Intelligence collaboration to maintain effective cyber defenses.
