探索基于注意力分类器的联邦-持续学习节点在鲁棒网络钓鱼检测中的有效性：一项实证研究
Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation
AI总结：
本文提出联邦-持续学习混合范式与带残差连接的注意力分类器，用于鲁棒网络钓鱼检测，在LwF策略下取得0.93准确率、0.90精确率、0.96召回率和0.93 F1分数，优于传统方法。
摘要：
网络钓鱼构成一种动态威胁，要求检测系统能够快速适应最新攻击手段。传统的积累数据并周期性重新训练模型的方法已难以跟上威胁变化。我们提出一种结合联邦学习与持续学习的新范式，使分布式节点能够在不断到来的新钓鱼数据流上持续更新模型，而无需积累数据。这些本地自适应模型随后通过联邦学习在中央服务器上进行聚合。为提升检测能力，我们引入了一种专为网络钓鱼定制的、带残差连接的基于注意力的分类器模型，利用注意力机制捕捉复杂的钓鱼模式。我们通过实证研究，在多种持续学习策略（累积、重放、MIR、LwF）和模型架构上评估了这一混合学习范式。我们的主要贡献包括：（1）一种用于鲁棒网络钓鱼检测的新型混合联邦-持续学习范式；（2）一种专为该任务设计的新型注意力+残差连接模型，在LwF策略下达到0.93的准确率、0.90的精确率、0.96的召回率和0.93的F1分数，在检测新兴钓鱼威胁的同时保留过去知识，优于传统方法。
英文摘要：
Web phishing poses a dynamic threat, requiring detection systems to quickly adapt to the latest tactics. Traditional approaches of accumulating data and periodically retraining models are outpaced. We propose a novel paradigm combining federated learning and continual learning, enabling distributed nodes to continually update models on streams of new phishing data, without accumulating data. These locally adapted models are then aggregated at a central server via federated learning. To enhance detection, we introduce a custom attention-based classifier model with residual connections, tailored for web phishing, leveraging attention mechanisms to capture intricate phishing patterns. We evaluate our hybrid learning paradigm across continual learning strategies (cumulative, replay, MIR, LwF) and model architectures through an empirical investigation. Our main contributions are: (1) a new hybrid federated-continual learning paradigm for robust web phishing detection, and (2) a novel attention + residual connections based model explicitly designed for this task, attaining 0.93 accuracy, 0.90 precision, 0.96 recall and 0.93 f1-score with the LwF strategy, outperforming traditional approaches in detecting emerging phishing threats while retaining past knowledge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
