AI and Cyber-Security: Enhancing threat detection and response with machine learning.

Abstract

As cyber threats continue to evolve and become more sophisticated, traditional security measures are no longer sufficient to protect networks and sensitive data. Artificial intelligence (AI) and machine learning (ML) techniques offer powerful tools to enhance cyber security by enabling more effective and efficient threat detection and response. This paper provides an overview of the current state of AI and ML in cyber security, discussing key techniques, applications, challenges, and future directions. We review ML algorithms used for tasks such as anomaly detection, malware classification, and network intrusion detection. Case studies are presented showing the successful implementation of AI/ML in real-world cyber security systems. Limitations and challenges are also discussed, including the need for large labelled datasets, adversarial attacks on ML models, and the difficulty of interpreting black-box ML models. Finally, we highlight promising research directions, such as explainable AI for cyber security, unsupervised learning approaches, and the integration of ML with other security tools and frameworks. AI and ML will play an increasingly crucial role in cyber security going forward, and ongoing research will help unlock their full potential for safeguarding our digital infrastructure.