Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation

arXiv:2405.03537v1 Announce Type: new
Abstract: 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 …

abstract adapt arxiv attention classifier continual cs.ai cs.lg data detection dynamic federated learning investigation latest nodes novel paradigm phishing phishing detection retraining robust systems tactics threat type web