GNNs for Network Security (and Privacy) GRAPHS4SEC

用于网络安全（和隐私）的 GNN GRAPHS4SEC

• 批准号: EP/Y036050/1
• 负责人: Hamed Haddadi
• 金额: $ 41.57万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY036050%2F1
• 关键词: GNNs; Network; Security; Privacy; GRAPHS4SEC

The application of Artificial Intelligence (AI) and Machine Learning (ML) to network security (AI4SEC) is paramount against cybercrime. While AI/ML is mainstream in domains such as computer vision and natural language processing, traditional AI/ML has produced below-par results in AI4SEC. Solutions do not properly generalize, are ineffective in real deployments, and are vulnerable to adversarial attacks. A fundamental limitation is the lack of AI/ML technology specific to network security.Due to their unique ability to learn and generalize over graph-structured information, graph- learning approaches, and in particular Graph Neural Networks (GNNs), have recently enabled groundbreaking applications in multiple fields where data are generally represented as graphs. Network security data are intrinsically relational, and initial research suggests that graph- structured representations and GNNs have the potential to become foundational to AI4SEC, in the way convolutional and recursive networks were to computer vision and natural language processing.The goal of GRAPHS4SEC is to leverage graph data representations and modern GNN technology to conceive a new breed of robust GNN-based network security methods which could radically advance the AI4SEC practice. The objectives of GRAPHS4SEC are: (a) to investigate algorithmic methods that facilitate modeling and learning from graph-based network security data; (b) to compare the benefits and overheads of GNN-based AI4SEC to traditional AI/ML in terms of detection performance, generalization, scalability, and robustness against adversarial attacks; (c) to showcase the benefits and improvements of GRAPHS4SEC technology in four critical, real-world network security applications with significant impact for society, considering (in particular) the detection and early mitigation of phishing and fake/malicious websites, a threat among the most popular and society-wide harmful in today's Internet.

将人工智能（AI）和机器学习（ML）应用于网络安全（AI 4SEC）对于打击网络犯罪至关重要。虽然AI/ML在计算机视觉和自然语言处理等领域是主流，但传统AI/ML在AI 4SEC中的结果低于标准。解决方案不能正确推广，在真实的部署中无效，并且容易受到对抗性攻击。一个根本的限制是缺乏针对网络安全的AI/ML技术。由于其独特的学习和概括图结构信息的能力，图学习方法，特别是图神经网络（GNN），最近在数据通常表示为图的多个领域中实现了突破性的应用。网络安全数据本质上是相关的，初步研究表明，图结构表示和GNN有可能成为AI 4SEC的基础，GRAPHS 4SEC的目标是利用图形数据表示和现代GNN技术来构思一种新的强大GNN-基于网络安全的方法，可以从根本上推进AI 4SEC实践。GRAPHS 4SEC的目标是：（a）研究有助于从基于图的网络安全数据中建模和学习的算法方法;（B）比较基于GNN的AI 4SEC与传统AI/ML在检测性能，泛化，可扩展性和对抗性攻击的鲁棒性方面的优势和开销;（c）展示GRAPHS 4SEC技术在四个对社会有重大影响的关键现实网络安全应用中的好处和改进，考虑到（特别是）网络钓鱼和虚假/恶意网站的检测和早期缓解，这是当今互联网中最受欢迎和社会范围内最有害的威胁之一。