Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The cybersecurity environments have been radically transformed by digital transformation, which has brought about challenges never seen before and cannot be properly addressed using traditional security constructs. The modern attack vectors used by cybercriminals are advanced to the point where they can easily discern traditional rule-based systems and signature-based detection mechanisms and, therefore, require radical approaches to defense. Artificial intelligence models based on transformers that were initially created to work with natural language processing have become effective tools in detecting anomalies in cloud security and fraud prevention systems. These architectures exploit self-attention techniques to handle the sequential data with great speed, allowing real-time threat detection in the distributed cloud scenarios. Application of transformer models in cybersecurity settings can be seen to exhibit better performance with regard to processing multi-dimensional and complex security data streams than conventional methods. Transformer architecture-based real-time data processing models are capable of processing large amounts of streaming data and staying at low latencies and high threat detection accuracies. Advanced detection tools have sequence modeling and bidirectional analysis, self learning, and contextual anomaly scoring to detect advanced attack patterns. The industry applications are in the financial services, cloud security, retail e-commerce, and healthcare compliance industries, where transformer-based anomaly detection systems that can adapt in line with the current threat scenarios are useful in offering proactive defense features against the new cyber threats.