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.

人工智能（AI）和机器学习（ML）在网络安全性（AI4SEC）中的应用至关重要。尽管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。解决方案没有正确地概括，在实际部署中无效，并且容易受到对抗性攻击的影响。一个基本限制是缺乏针对网络安全性的AI/ML技术。其独特的学习能力，超过了图形结构的信息，图形学习方法，尤其是图形神经网络（GNN），最近在多个领域中启用了数据，这些领域通常以数据表示为图形。网络安全数据在本质上是关系的，并且初步研究表明，图形结构化表示和GNN有可能成为AI4SEC的基础，以卷积和递归网络的方式进行计算机视觉和自然语言处理。 Graphs4sec的目标是：（a）研究算法方法，以促进基于图形的网络安全数据进行建模和学习； （b）将基于GNN的AI4SEC与传统AI/ML的好处和间接开销，以检测性能，概括，可扩展性和鲁棒性针对对抗性攻击； （c）在四个关键的，现实的网络安全应用程序中展示Graphs4sec技术的好处和改进，对社会产生了重大影响，考虑（尤其是）（尤其是）检测和早期缓解网络钓鱼和假/恶意网站，这是当今互联网上最受欢迎和最受欢迎的有害的威胁。