A Comprehensive Analysis of Adversarial Attacks against Spam Filters

• URL: http://arxiv.org/abs/2505.03831v1
• Date: Sun, 04 May 2025 11:38:13 GMT
• Title: A Comprehensive Analysis of Adversarial Attacks against Spam Filters
• Authors: Esra Hotoğlu, Sevil Sen, Burcu Can,
• Abstract summary: This study investigates the impact of adversarial attacks on deep learning-based spam detection systems using real-world datasets.<n>Novel scoring functions, including spam weights and attention weights, are introduced to improve attack effectiveness.
• Score: 3.5307531256684848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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