Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs

Abstract: The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infrastructures, leading to compromised data, financial losses, potential harm to individuals, and disruptions in essential services. The current security landscape demands the urgent development of a holistic security assessment solution that encompasses vulnerability analysis and investigates the potential exploitation of these vulnerabilities as attack paths. In this paper, we propose Graphene, an advanced system designed to provide a detailed analysis of the security posture of computing infrastructures. Using user-provided information, such as device details and software versions, Graphene performs a comprehensive security assessment. This assessment includes identifying associated vulnerabilities and constructing potential attack graphs that adversaries can exploit. Furthermore, Graphene evaluates the exploitability of these attack paths and quantifies the overall security posture through a scoring mechanism. The system takes a holistic approach by analyzing security layers encompassing hardware, system, network, and cryptography. Furthermore, Graphene delves into the interconnections between these layers, exploring how vulnerabilities in one layer can be leveraged to exploit vulnerabilities in others. In this paper, we present the end-to-end pipeline implemented in Graphene, showcasing the systematic approach adopted for conducting this thorough security analysis.

• The paper introduces Prometheus, an AI-driven system that uses NER and word embeddings to map vulnerabilities and generate detailed attack graphs.
• It employs a methodology that constructs both cumulative and isolated graphs across hardware, system, network, and cryptographic layers.
• The analysis quantifies risks with a security scoring mechanism, offering actionable insights for prioritizing network security measures.

Introduction

In the field of enterprise network security, the landscape is rapidly evolving, presenting challenges in safeguarding digital assets. The Prometheus system emerges as an innovative answer to these challenges. Relying on information such as device specifics and software versions provided by the user, Prometheus performs a comprehensive security assessment. This includes identifying vulnerabilities and modeling potential attack graphs. Delving deeper, it evaluates exploitability of these attack paths and provides a quantifiable security score. Notably, this system focuses on an exhaustive analysis across layers—namely hardware, system, network, and cryptography—and investigates the vulnerability links across them.

Problem Scope and Challenges

With the current surge in network complexity and the corresponding vulnerabilities, there exists an imperative need for an all-inclusive system capable of not only identifying infrastructure-specific vulnerabilities but also scrutinizing the exploit sequences. Addressing challenges such as the natural language descriptions of vulnerabilities and the manual effort required in deducing potential attack paths makes for a highly sophisticated task. Prometheus's objective is to leverage AI techniques in capturing semantics of vulnerabilities, link them into an attack sequence and ultimately deduce a risk score that can guide mitigation efforts.

Our Approach

To conquer these challenges, Prometheus deploys named entity recognition (NER) to parse the semantics of vulnerabilities from national vulnerability databases, encoding this information for further analysis. It utilizes word embeddings to semantically match related vulnerabilities, thus constructing coherent attack graphs. These graphs emerge in two forms: cumulative across layers and isolated within individual layers, serving distinctive analytical purposes. The analysis these graphs enable is deeply layered, revealing exploitable sequences and the impact on the infrastructure. Such granular insight is essential for prioritization in security measures.

Roadmap

Prometheus articulates a roadmap starting with motivation and background, flowing to a comprehensive presentation of its pipeline and in-depth technical processes. This trajectory includes machine learning processing for entity recognition and word embedding application for edge construction in attack graphs. Risk analysis methodology incorporated into Prometheus is critical in inferring the network's security posture, identifying high-risk paths and suggesting immediate actions. Results evaluation and related work are discussed to engage the reader with the context and potential of Prometheus.

Through the systematic analyses enabled by Prometheus, security assessments can now reach deeper, leveraging AI to interconnect vulnerabilities across an infrastructure. As networks grow in complexity, such innovation is not only welcome but required, making Prometheus a significant stride toward mature cybersecurity analytics.