Adapting to Change

Abstract

The rapid evolution of cyber threats in digital communication necessitates robust and adaptive natural language processing (NLP) models, especially for spam detection. This chapter explores the effectiveness and sustainability of adversarial training in NLP models within dynamic spam detection contexts. The authors investigate how adversarially trained models illustrate the concept drift phenomenon. The findings reveal significant insights into the limitations and potential of adversarial training, providing a nuanced understanding of its long-term implications in real-world deployment scenarios. This research contributes to the broader understanding of NLP model resilience, emphasizing the necessity of continuous model evolution to maintain efficacy in changing cyber environments.