Ensemble Learning-Based Methods for Detecting APTs

Author: Ahzaaf S. | Yenepoya University

Email: ahzaaf.ajin@gmail.com

Abstract

Advanced Persistent Threats (APTs) pose a significant risk in cybersecurity due to their ability to evadetraditional security mechanisms. Their stealthy nature makes them difficult to detect using conventional rule-based approaches. This study explores machine learning for APT detection by analyzing large-scale networktraffic and system logs. We employ ensemble learning models to improve predictive accuracy and reduce falsepositives. By leveraging diverse real-world datasets, we assess the model’s performance in terms of accuracy,speed, and effectiveness. Our findings highlight ensemble learning as a scalable solution for APT detection.Future work will explore advanced ensemble techniques and real-world validation to enhance detection systemsagainst evolving APT threats (PDF) Ensemble Learning-Based Methods for Detecting Advanced Persistent Threats

Introduction

Cybersecurity threats continue to evolve, with attackers developing new techniques to bypass traditionaldefenses. Among these, Advanced Persistent Threats (APTs) pose a serious challenge due to their stealthy andprolonged presence in compromised systems. APTs often remain undetected for months or even years, allowingattackers—often state-sponsored groups—to steal sensitive information or disrupt critical infrastructure. Thesegroups strategically infiltrate networks, moving laterally across systems while avoiding detection The MITREATT&CK framework identifies over 90 APT groups worldwide, many backed by well-funded nation-states suchas China, Russia, Iran, and North Korea. Groups like APT28, APT29, Lazarus Group, and APT41 have beenresponsible for high-profile cyberattacks, often leveraging zero-day exploits and advanced evasion techniques,such as fileless malware and Living-off-the-Land (LotL) tactics. A notable example is APT29 (Cozy Bear),which remained undetected in U.S. government systems for months during the SolarWinds attack. Traditionalrule-based security systems struggle to detect APTs due to their ability to exploit unknown vulnerabilities andoperate at a slow, calculated pace. This often results in high false-positive rates, making it difficult for securityteams to distinguish legitimate activity from malicious behavior. Given the vast amount of security datagenerated daily, manual detection is nearly impossible. The increase in the sophistication of APT’s makes it anightmare to every Security personals, To address these challenges there is a need for more advanced andeffective mechanism for detection of these APT’s . This is the reason why most of cybersecurity solution areadapting and leveraging AI-driven threat detection products. This study tends to explore how an ensemble ofmachine learning algorithms can improve APT detection rates while reducing false positives. The primaryobjective of this research is to: 1) Investigate the effectiveness of ensemble machine learning algorithms fordetection of detection of APT’s in network traffic data. 2) Evaluate the performance of ensemble models,specifically for achieving high detection rates for APT activity while minimizing false positive rates at the sam

Ensemble Learning-Based Methods for Detecting Advanced Persistent Threats.

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