Adapting to Change: Assessing the Longevity and Resilience of Adversarially Trained NLP Models in Dynamic Spam Detection Environments

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Author(s): Mahmoud Basharat (Capitol Technology University, USA & Houston Community College, USA)and Marwan Omar (Capitol Technology University, USA & Illinois Institute of Technology, USA)
Copyright: 2024
Pages: 17
Source title: Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology
Source Author(s)/Editor(s): Darrell Norman Burrell (Marymount University, USA)
DOI: 10.4018/979-8-3693-1906-2.ch009

Keywords: Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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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.

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Adapting to Change: Assessing the Longevity and Resilience of Adversarially Trained NLP Models in Dynamic Spam Detection Environments

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