ToLeRating UR-STD

TL;DR

This paper introduces a novel anomaly detection approach inspired by human immune system Toll-Like Receptors, aiming to improve detection and classification of cyber threats by leveraging a new paradigm of Uncertain Risk of Suspicion, Threat, and Danger.

Contribution

It presents a new immune-inspired anomaly detection model that classifies threats based on a taxonomy of digital Acytota, enhancing detection accuracy and reducing administrator burden.

Findings

Receptor-based anomaly detection improves threat classification.

The model effectively distinguishes normal and malicious behaviors.

A taxonomy of digital Acytota supports the receptor creation.

Abstract

A new emerging paradigm of Uncertain Risk of Suspicion, Threat and Danger, observed across the field of information security, is described. Based on this paradigm a novel approach to anomaly detection is presented. Our approach is based on a simple yet powerful analogy from the innate part of the human immune system, the Toll-Like Receptors. We argue that such receptors incorporated as part of an anomaly detector enhance the detector's ability to distinguish normal and anomalous behaviour. In addition we propose that Toll-Like Receptors enable the classification of detected anomalies based on the types of attacks that perpetrate the anomalous behaviour. Classification of such type is either missing in existing literature or is not fit for the purpose of reducing the burden of an administrator of an intrusion detection system. For our model to work, we propose the creation of a taxonomy of the digital Acytota, based on which our receptors are created.