Managing Advanced Persistent Threats (APTs): Detection Strategies and Network Defense Mechanisms

Lun Wang; Qiang Chen; Daoming Li

doi:10.5281/zenodo.13212276

Authors

Lun Wang Meta Platforms
Qiang Chen Sun Yat-sen University
Daoming Li Shanghai Jiao Tong University

DOI:

https://doi.org/10.5281/zenodo.13212276

ARK:

https://n2t.net/ark:/40704/JETBM.v1n4a02

References:

32

Keywords:

Advanced Persistent Threats, APTs, Cybersecurity, Detection Strategies, Network Defense Mechanisms, intrusion Detection Systems, intrusion Prevention Systems, Endpoint Detection and Response, Network Segmentation, Micro-segmentation

Abstract

Advanced Persistent Threats (APTs) represent one of the most significant challenges in cybersecurity today. These threats are characterized by their stealthy, sophisticated, and persistent nature, often targeting high-value entities such as government institutions, financial systems, and critical infrastructure. This paper explores the nature of APTs, focusing on detection strategies and network defense mechanisms. Through a comprehensive review of existing literature and case studies, the paper presents an in-depth analysis of how APTs operate and how organizations can effectively detect and mitigate these threats. The paper also discusses the implications of emerging technologies and future directions in APT defense.
This study highlights the evolving tactics used by APT groups, emphasizing the need for adaptive and layered security approaches. Moreover, it underscores the importance of integrating threat intelligence and automated response systems into existing cybersecurity frameworks. By examining both successful and failed defense strategies in past APT incidents, this paper provides actionable insights for enhancing organizational resilience against such sophisticated threats. The findings aim to contribute to the ongoing discourse on improving cybersecurity practices and inform the development of more robust, future-proof defense mechanisms.

Author Biographies

Lun Wang, Meta Platforms

Electrical and computer engineering, Meta Platforms, USA.

Qiang Chen, Sun Yat-sen University

School of Space and Network at Sun Yat-sen University, Shenzhen.

Daoming Li, Shanghai Jiao Tong University

School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai.

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