Cross-Layer Threat Detection Framework For Multi-Domain Apts Using Network Telemetry And Data Mining

Abstract

Advanced Persistent Threats (APTs) present a considerable challenge to cybersecurity due to their elusive and multi-faceted nature. Conventional security measures frequently struggle to identify APTs due to their capacity to avoid signature-based detection and exploit vulnerabilities across various layers of the network. This paper introduces a Cross-Layer Threat Detection Framework that utilizes network telemetry and data mining techniques to recognize and address multi-domain APT activities. The framework consolidates data from numerous network layers, such as application, transport, and network layers, to construct a comprehensive view of potential threats. By employing sophisticated machine learning and data mining algorithms, the system identifies unusual behavior patterns that signal APTs. Moreover, real-time network telemetry data improves situational awareness, facilitating proactive threat hunting and mitigation efforts. Experimental findings reveal the framework’s efficacy in identifying stealthy APT activities with high precision and minimal false positive rates. The proposed method strengthens cybersecurity defenses by offering adaptive, scalable, and intelligent threat detection against complex APT campaigns.