柴一栋:An Explainable Multi-Modal Hierarchical Attention Model for Developing Phishing Threat Intelligence

发布时间:2021-12-17浏览次数:458


时  间:20211222日(周三)1830-2030

腾讯会议ID(线上会议):176 546 184

主讲人:柴一栋 合肥工业大学管理学院 研究员

主持人:张明月 副教授

主  题An Explainable Multi-Modal Hierarchical Attention Model for Developing Phishing Threat Intelligence

讲座摘要

Phishing website attack, as one of the most persistent forms of cyber threats, evolves and remains a major cyber threat. Previous deep representation-based methods fail to analyze two important modalities of website content: textual information and visual design. Moreover, the interpretability of these deep learning based methods is limited. As such, we propose a multi-modal hierarchical attention model (MMHAM) which jointly learns the deep fraud cues from the three major modalities of website content for phishing website detection. Specifically, MMHAM features an innovative shared dictionary learning approach for aligning representations from different modalities in the attention mechanism. In our evaluation experiments, the proposed MMHAM provided a hierarchical interpretability system from which we could develop phishing threat intelligence to inform phishing websites detection at different levels.

嘉宾简介:


柴一栋,合肥工业大学黄山青年学者,研究员,博士生导师。博士毕业于清华大学经管学院管理科学与工程系,研究领域包括机器学习、大数据管理与技术、商务智能、网络空间管理、智慧医疗等。主持国家自然科学青年基金、校内青年学者支持培育基金等三项。以第一作者或通讯作者发表研究成果于MISQ(UTD 24, ABS 4*), IEEE TDSC(CCF A), JMIS(FT 50)等国际顶级期刊及ICISWITSPACISCSWIMINFORMS Workshop on Data Science等国际会议。担任POMIEEE TDSC等国际顶级期刊审稿人。

(承办:信息管理与决策科学系,人工智能数据工程中心,人工智能与数据科学应用实验室,科研与师资发展办公室)



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