Bio-Inspired Endpoint Security Detection Method Based on an Ecosystem Model

In recent years, cyber-attacks have become increasingly sophisticated as technologies such as generative AI and IoT evolve alongside the progress of Digital Transformation (DX).Traditional perimeter security measures have shown limitations due to the widespread adoption of remote work and cloud services, making endpoint management particularly challenging.In response, the zero-trust model has gained attention, but significant challenges remain, including improving the accuracy of endpoint verification.This study explores flexible defense mechanisms inspired by ecosystems and proposes a novel security detection method.Specifically, we integrate Software-Defined Perimeter (SDP), a zero-trust framework, into a fog-edge computing environment as a testbed for validation.Our method leverages three key functions mimicking biological defense mechanisms: an immune-like predictive failure detection based on TPO-based conditions (Time, Place, Occasion), an autonomous failure detection inspired by inter-organ networks, and a collaborative detection function akin to plant chemical signaling.These functions enhance endpoint management policy fine-tuning, a critical aspect of the SDP controller, improving endpoint status detection accuracy.Furthermore, we conduct qualitative feasibility-based evaluations of the proposed approach.The results suggest its potential applicability to enhancing security in SDP-based environments against a wide range of endpoint-related threats, including ransomware, insider threats, and advanced persistent attacks.