Adaptive Spam Detection Inspired by a Cross-Regulation Model of Immune Dynamics: A Study of Concept Drift

TL;DR

This paper introduces a bio-inspired adaptive spam detection algorithm based on immune system models, demonstrating competitive performance against traditional classifiers across multiple email datasets.

Contribution

It presents a novel spam detection approach inspired by the cross-regulation immune model, with preliminary testing on diverse email corpora.

Findings

Competitive performance against Naive Bayes and previous methods

Effective handling of concept drift in email data

Potential for bio-inspired algorithms in binary classification

Abstract

This paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the crossregulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our results statically and dynamically with those obtained by the Naive Bayes classifier and another binary classification method we developed previously for biomedical text-mining applications. We show that the cross-regulation model is competitive against those and thus promising as a bio-inspired algorithm for spam detection in particular, and binary classification in general.