The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilities for malicious actors to exploit. Spam emails, a form of unsolicited correspondence often bearing malicious intent towards recipients, have been an ongoing challenge for email users since the inception of email technology, and this problem has been exacerbated by the growth of the digital landscape. Email spam filters are integral components of email clients, engineered to identify potentially harmful messages and alert users to their malicious content. Phishing, frequently the initial phase of malware-based attacks, is evolving rapidly, with malware becoming increasingly sophisticated over time. A widely adopted approach for detecting malicious activity within malware and spam domains is the application of machine learning. Our aim is to assess the impact of the evolution within the spam email domain on these machine learning-based detection systems and to explore strategies for mitigating associated performance degradation.

翻译：数字领域的扩展导致了数字通信的显著增长，电子邮件已成为其中最突出的渠道之一。电子邮件的普及在职业和个人语境中均显而易见，从而为恶意行为者创造了大量可乘之机。垃圾邮件作为一种通常带有恶意意图的未经请求的通信形式，自电子邮件技术诞生以来一直是用户面临的持续挑战，这一问题随着数字景观的扩张而日益加剧。电子邮件垃圾邮件过滤器是邮件客户端的基本组件，旨在识别潜在有害信息并提醒用户其恶意内容。网络钓鱼——通常是基于恶意软件攻击的初始阶段——正在快速演变，恶意软件也随时间推移变得日益复杂。在恶意软件和垃圾邮件领域中，检测恶意活动的一种广泛采用的方法是应用机器学习。我们的目标是评估垃圾邮件领域演变对这些基于机器学习的检测系统的影响，并探索缓解相关性能下降的策略。