AI-Driven Security Measures for Proactive Threat Detection

EasyChair Preprint 14366

Abstract

In the rapidly evolving landscape of cybersecurity, traditional threat detection methods often struggle to keep pace with sophisticated and emerging threats. This paper explores the integration of artificial intelligence (AI) into security measures for proactive threat detection, aiming to enhance the efficacy and responsiveness of security systems. We present a comprehensive review of AI-driven approaches, including machine learning algorithms, behavioral analytics, and anomaly detection techniques, which leverage vast amounts of data to identify potential threats before they manifest. The study discusses the architecture of AI-based security frameworks, their application in real-time threat analysis, and their ability to adapt to new and unforeseen attack vectors. Additionally, we evaluate the effectiveness of these technologies through case studies and comparative analysis with traditional methods, highlighting their strengths and limitations. The findings suggest that AI-driven security measures offer significant advancements in threat detection capabilities, providing a crucial edge in the ongoing battle against cyber threats. This paper concludes with recommendations for the implementation of AI-driven security solutions and future research directions to address current challenges and enhance the resilience of cybersecurity infrastructures.

Keyphrases: Cybersecurity Automation, Intelligent Threat Response, Next-Gen Defense Mechanisms

@booklet{EasyChair:14366,
  author    = {Oluwaseun Abiade},
  title     = {AI-Driven Security Measures for Proactive Threat Detection},
  howpublished = {EasyChair Preprint 14366},
  year      = {EasyChair, 2024}}