Cybersecurity Entity Alignment via Masked Graph Attention Networks

• URL: http://arxiv.org/abs/2207.01434v1
• Date: Mon, 4 Jul 2022 14:19:32 GMT
• Title: Cybersecurity Entity Alignment via Masked Graph Attention Networks
• Authors: Yue Qin and Xiaojing Liao
• Abstract summary: Vulnerability information is often recorded by multiple channels, including government vulnerability repositories, individual-maintained vulnerability-gathering platforms, or vulnerability-disclosure email lists and forums. Efforts to automatically gather such information are impeded by the limitations of today's entity alignment techniques. We propose the first cybersecurity entity alignment model, CEAM, which equips GNN-based entity alignment with two mechanisms: asymmetric masked aggregation and partitioned attention.
• Score: 22.290325364132052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cybersecurity vulnerability information is often recorded by multiple channels, including government vulnerability repositories, individual-maintained vulnerability-gathering platforms, or vulnerability-disclosure email lists and forums. Integrating vulnerability information from different channels enables comprehensive threat assessment and quick deployment to various security mechanisms. Efforts to automatically gather such information, however, are impeded by the limitations of today's entity alignment techniques. In our study, we annotate the first cybersecurity-domain entity alignment dataset and reveal the unique characteristics of security entities. Based on these observations, we propose the first cybersecurity entity alignment model, CEAM, which equips GNN-based entity alignment with two mechanisms: asymmetric masked aggregation and partitioned attention. Experimental results on cybersecurity-domain entity alignment datasets demonstrate that CEAM significantly outperforms state-of-the-art entity alignment methods.

Related papers

• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Complete Security and Privacy for AI Inference in Decentralized Systems [14.526663289437584]
  Large models are crucial for tasks like diagnosing diseases but tend to be delicate and not very scalable. Nesa solves these challenges with a comprehensive framework using multiple techniques to protect data and model outputs. Nesa's state-of-the-art proofs and principles demonstrate the framework's effectiveness.
  arXiv  Detail & Related papers  (2024-07-28T05:09:17Z)
• SeCTIS: A Framework to Secure CTI Sharing [13.251593345960265]
  The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to leaks of both proprietary and confidential data. We design a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing) to enable businesses to collaborate, preserving the privacy of their CTI data.
  arXiv  Detail & Related papers  (2024-06-20T08:34:50Z)
• SETC: A Vulnerability Telemetry Collection Framework [0.0]
  This paper introduces the Security Exploit Telemetry Collection (SETC) framework. SETC generates reproducible vulnerability exploit data at scale for robust defensive security research. This research enables scalable exploit data generation to drive innovations in threat modeling, detection methods, analysis techniques, and strategies.
  arXiv  Detail & Related papers  (2024-06-10T00:13:35Z)
• Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
  We investigate the security implications of and software-based Open Radio Access Network (RAN) systems. We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
  arXiv  Detail & Related papers  (2024-05-03T07:18:45Z)
• Secure Aggregation is Not Private Against Membership Inference Attacks [66.59892736942953]
  We investigate the privacy implications of SecAgg in federated learning. We show that SecAgg offers weak privacy against membership inference attacks even in a single training round. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection.
  arXiv  Detail & Related papers  (2024-03-26T15:07:58Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• CVE representation to build attack positions graphs [0.39945675027960637]
  In cybersecurity, CVEs (Common Vulnerabilities and Exposures) are publicly disclosed hardware or software vulnerabilities. This article points out that these vulnerabilities should be described in greater detail to understand how they could be chained together in a complete attack scenario.
  arXiv  Detail & Related papers  (2023-12-05T08:57:14Z)
• Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling [6.0897744845912865]
  Cyber Threat Intelligence (CTI) has been represented as one of the proactive and robust mechanisms. Our goal is to identify and explore relevant CTI from hacker forums by using different supervised and unsupervised learning techniques.
  arXiv  Detail & Related papers  (2021-08-16T02:30:29Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.