Year: 2024
Type: conference-paper
Abstract: This paper examines advanced intrusion detection systems (IDS) essential for protecting telemetry enterprise networks against sophisticated cyber threats. It highlights the shift from traditional IDS methods to advanced techniques enhanced by artificial intelligence (AI) and machine learning (ML), focusing on the unique challenges posed by the scale and complexity of telemetry data. We analyze the effectiveness of behavioral analysis, predictive analytics, and threat intelligence integration in telemetry environments alongside a comparative review of current technologies and tools. The paper illustrates successful implementations and the benefits of advanced IDS through case studies. Our research indicates that despite existing challenges, integrating AI, ML, and analytics into IDS presents promising avenues for improving cybersecurity in telemetry networks. We conclude with actionable recommendations for cybersecurity practitioners and suggest directions for future research.
Author: Okonkwo, Favour
Institution: Morgan State University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Morgan State University
OpenAlex ID: W7007795152
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