A Comprehensive Analysis of Adversarial Attacks against Spam Filters
This work addresses the problem of adversarial threats to email security for users, but it is incremental as it builds on existing research with new analysis and scoring methods.
The study investigated the impact of adversarial attacks on deep learning-based spam detection systems, evaluating six models on real-world datasets and introducing novel scoring functions to improve attack effectiveness, revealing vulnerabilities in spam filters.
Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates the impact of adversarial attacks on deep learning-based spam detection systems using real-world datasets. Six prominent deep learning models are evaluated on these datasets, analyzing attacks at the word, character sentence, and AI-generated paragraph-levels. Novel scoring functions, including spam weights and attention weights, are introduced to improve attack effectiveness. This comprehensive analysis sheds light on the vulnerabilities of spam filters and contributes to efforts to improve their security against evolving adversarial threats.