GNNs for Network Security (and Privacy) GRAPHS4SEC

人工智能（AI）和机器学习（ML）在网络安全（AI4SEC）中的应用对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，但传统的AI/ML在AI4SEC中产生了低于标准的结果。解决方案不能正确地泛化，在实际部署中是无效的，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泛化图结构信息的能力，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互关联的，初步研究表明，图结构表示和gnn有可能成为AI4SEC的基础，就像卷积和递归网络对计算机视觉和自然语言处理的影响一样。GRAPHS4SEC的目标是利用图数据表示和现代GNN技术来构思一种新的基于GNN的健壮网络安全方法，这可以从根本上推进AI4SEC的实践。GRAPHS4SEC的目标是：(a)研究促进基于图的网络安全数据建模和学习的算法方法；(b)比较基于gnn的AI4SEC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GRAPHS4SEC技术在四种对社会有重大影响的关键现实网络安全应用中的优势和改进，特别是考虑到（特别是）检测和早期缓解网络钓鱼和虚假/恶意网站，这是当今互联网中最受欢迎和对社会有害的威胁之一。

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.