Preprint Review Version 1 This version is not peer-reviewed

Adapting to the Evolution of Cyber Threats: Insights for 2024 and Beyond

Version 1 : Received: 4 September 2024 / Approved: 4 September 2024 / Online: 4 September 2024 (09:30:50 CEST)

How to cite: Biplob, M. B.; Akther, M.; Farabi, A. M.; Ramisha, J. F. Adapting to the Evolution of Cyber Threats: Insights for 2024 and Beyond. Preprints 2024, 2024090350. https://doi.org/10.20944/preprints202409.0350.v1 Biplob, M. B.; Akther, M.; Farabi, A. M.; Ramisha, J. F. Adapting to the Evolution of Cyber Threats: Insights for 2024 and Beyond. Preprints 2024, 2024090350. https://doi.org/10.20944/preprints202409.0350.v1

Abstract

This paper provides a thorough examination of important themes critical to understanding and tackling modern cyber threats. It explores the revolutionary significance of AI and machine learning in cybersecurity, emphasizing their dynamic influence on defense systems. It also addresses how zero-trust designs have caused a paradigm change in network security, highlighting the significance of trust verification in current cybersecurity frameworks. Furthermore, the article investigates the integration of cybersecurity with corporate strategy, arguing for a collaborative approach to efficiently mitigating risks while minimizing operational impact. Furthermore, it investigates the idea of cyber resilience, highlighting an organization's ability to recover from assaults and adapt to changing threats. Furthermore, the paper discusses the issues faced by deepfakes in the synthetic media revolution and emphasizes the rising worry about data privacy in the digital era. Finally, it looks into the introduction of behavioral biometrics as a possible way to improve authentication and security methods. This paper contributes to the discussion of effective tactics for mitigating cyber threats in today's digital ecosystem by providing a thorough overview of these subjects.

Keywords

AI-powered threat detection; cybersecurity frameworks; behavioral biometrics; risk mitigation; cyber resilience; artificial intelligence; machine learning

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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