研究深度学习垃圾邮件过滤器的对抗攻击，揭示其安全漏洞。
A Comprehensive Analysis of Adversarial Attacks against Spam Filters
- 用真实数据集测试六种模型，从词到段落级发动对抗攻击。
- 引入垃圾邮件权重和注意力权重新评分函数，提升攻击成功率。
- 发现现有过滤系统易受多层级攻击，适合安全研究人员参考。
深度学习已革新电子邮件过滤技术，对防范垃圾邮件、恶意软件和网络钓鱼等网络威胁至关重要。然而，日益复杂的对抗攻击正严重威胁此类过滤器的有效性。本研究基于真实世界数据集，全面分析对抗攻击对深度学习垃圾邮件检测系统的影响。评估了六种主流深度学习模型，并在词、字符、句子及人工智能生成段落等多个层级实施攻击。提出新的评分函数，包括垃圾邮件权重和注意力权重，以增强攻击效果。该研究揭示了垃圾邮件过滤系统的潜在弱点，有助于提升其应对不断演进的对抗性威胁的能力。
原文摘要 · Abstract (English)
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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