Leveraging Machine Learning for Automated Threat Detection and Response

EasyChair Preprint 14924

Abstract

In an era of escalating cyber threats and sophisticated attack vectors, the need for effective and automated threat detection and response mechanisms has never been more critical. This paper explores the potential of leveraging machine learning (ML) technologies to enhance the automation and efficacy of threat detection and response systems. We examine various ML algorithms, including supervised and unsupervised learning, and their application to real-time threat analysis and mitigation. The paper details a framework for integrating ML models into existing security infrastructures, focusing on anomaly detection, pattern recognition, and predictive analytics to identify and respond to emerging threats. Additionally, we discuss the challenges associated with implementing ML in cybersecurity, such as data quality, model interpretability, and adversarial attacks. Through case studies and experimental results, we demonstrate how ML-driven approaches can significantly reduce false positives, improve detection accuracy, and accelerate incident response times. The findings suggest that machine learning offers a promising avenue for advancing automated threat management and fortifying defenses against an increasingly complex threat landscape.

Keyphrases: decision-making processes, organizational culture, security breaches

@booklet{EasyChair:14924,
  author    = {Adeyeye Barnabas},
  title     = {Leveraging Machine Learning for Automated Threat Detection and Response},
  howpublished = {EasyChair Preprint 14924},
  year      = {EasyChair, 2024}}