Risk mitigation approach to cyber threat using ai-driven models - Olanrewaju, Jesufemi;Togunde, Matthias Oluloni
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This systematic review evaluates the effectiveness of AI-driven models in mitigating evolving cyber threats, focusing on machine learning techniques like supervised, unsupervised, and deep learning. Deep learning excels in detecting complex threats like APTs and zero-day vulnerabilities, while supervised learning is effective for known threats but struggles with novel attacks. Unsupervised learning adapts well to dynamic environments but has higher false positive rates. The review proposes a multi-layered framework combining AI models with traditional security measures for enhanced threat…mehr

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Produktbeschreibung
This systematic review evaluates the effectiveness of AI-driven models in mitigating evolving cyber threats, focusing on machine learning techniques like supervised, unsupervised, and deep learning. Deep learning excels in detecting complex threats like APTs and zero-day vulnerabilities, while supervised learning is effective for known threats but struggles with novel attacks. Unsupervised learning adapts well to dynamic environments but has higher false positive rates. The review proposes a multi-layered framework combining AI models with traditional security measures for enhanced threat detection and response. Challenges such as data quality,algorithmic bias, and adversarial attacks must be addressed for optimal implementation. A hybrid approach is recommended for robust cybersecurity.
Autorenporträt
Jesufemi OlanrewajuAn experienced Cloud & Security Architect with over a decade of experience as a Cloud Solutions Architect and DevSecOps Engineer on AWS,Azure and GCP. Matthias Oluloni TogundeAn excellent GRC consultant with strong ability to impact knowledge. Responsible for IT process control that covers the delivery of services enterprisewide.