CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model

• URL: http://arxiv.org/abs/2309.02785v1
• Date: Wed, 6 Sep 2023 06:53:45 GMT
• Title: CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model
• Authors: Ehsan Aghaei, Ehab Al-Shaer
• Abstract summary: The paper introduces the TTPpredictor tool, which uses innovative techniques to analyze CVE descriptions and infer plausible TTP attacks resulting from CVE exploitation. TTPpredictor overcomes challenges posed by limited labeled data and semantic disparities between CVE and TTP descriptions. The paper presents an empirical assessment, demonstrating TTPpredictor's effectiveness with accuracy rates of approximately 98% and F1-scores ranging from 95% to 98% in precise CVE classification to ATT&CK techniques.
• Score: 2.1756081703276
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper addresses a critical challenge in cybersecurity: the gap between vulnerability information represented by Common Vulnerabilities and Exposures (CVEs) and the resulting cyberattack actions. CVEs provide insights into vulnerabilities, but often lack details on potential threat actions (tactics, techniques, and procedures, or TTPs) within the ATT&CK framework. This gap hinders accurate CVE categorization and proactive countermeasure initiation. The paper introduces the TTPpredictor tool, which uses innovative techniques to analyze CVE descriptions and infer plausible TTP attacks resulting from CVE exploitation. TTPpredictor overcomes challenges posed by limited labeled data and semantic disparities between CVE and TTP descriptions. It initially extracts threat actions from unstructured cyber threat reports using Semantic Role Labeling (SRL) techniques. These actions, along with their contextual attributes, are correlated with MITRE's attack functionality classes. This automated correlation facilitates the creation of labeled data, essential for categorizing novel threat actions into threat functionality classes and TTPs. The paper presents an empirical assessment, demonstrating TTPpredictor's effectiveness with accuracy rates of approximately 98% and F1-scores ranging from 95% to 98% in precise CVE classification to ATT&CK techniques. TTPpredictor outperforms state-of-the-art language model tools like ChatGPT. Overall, this paper offers a robust solution for linking CVEs to potential attack techniques, enhancing cybersecurity practitioners' ability to proactively identify and mitigate threats.

Related papers

• TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models [53.91006249339802]
  We propose a novel defense method called Test-Time Adversarial Prompt Tuning (TAPT) to enhance the inference robustness of CLIP against visual adversarial attacks. TAPT is a test-time defense method that learns defensive bimodal (textual and visual) prompts to robustify the inference process of CLIP. We evaluate the effectiveness of TAPT on 11 benchmark datasets, including ImageNet and 10 other zero-shot datasets.
  arXiv  Detail & Related papers  (2024-11-20T08:58:59Z)
• CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity [49.657358248788945]
  Textual descriptions in cyber threat intelligence (CTI) reports are rich sources of knowledge about cyber threats. Current CTI extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction. We propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models.
  arXiv  Detail & Related papers  (2024-10-28T14:18:32Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks [90.52808174102157]
  In safety-critical applications such as medical imaging and autonomous driving, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks. A notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks.
  arXiv  Detail & Related papers  (2024-05-14T18:05:19Z)
• TTPXHunter: Actionable Threat Intelligence Extraction as TTPs from Finished Cyber Threat Reports [3.2183320563774833]
  Knowing the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and translate it into a structured format. This research introduces a methodology named TTPXHunter for the automated extraction of threat intelligence in terms of Tactics, Techniques, and Procedures (TTPs) from finished cyber threat reports.
  arXiv  Detail & Related papers  (2024-03-05T19:04:09Z)
• Automated CVE Analysis for Threat Prioritization and Impact Prediction [4.540236408836132]
  We introduce our novel predictive model and tool (called CVEDrill) which revolutionizes CVE analysis and threat prioritization. CVEDrill accurately estimates the Common Vulnerability Scoring System (CVSS) vector for precise threat mitigation and priority ranking. It seamlessly automates the classification of CVEs into the appropriate Common Weaknession (CWE) hierarchy classes.
  arXiv  Detail & Related papers  (2023-09-06T14:34:03Z)
• Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques [77.34726150561087]
  We evaluate and compare different Natural Language Processing (NLP) and machine learning techniques used for security information extraction in research. Based on our investigations we propose a data processing pipeline that automatically classifies unstructured text according to attackers' tactics and techniques.
  arXiv  Detail & Related papers  (2022-07-18T09:59:21Z)
• EXTRACTOR: Extracting Attack Behavior from Threat Reports [6.471387545969443]
  We propose a novel approach and tool called provenanceOR that allows precise automatic extraction of concise attack behaviors from CTI reports. provenanceOR makes no strong assumptions about the text and is capable of extracting attack behaviors as graphs from unstructured text. Our evaluation results show that provenanceOR can extract concise graphs from CTI reports and can successfully be used by cyber-analytics tools in threat-hunting.
  arXiv  Detail & Related papers  (2021-04-17T18:51:00Z)
• V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities [7.906207218788341]
  We present a novel Transformer-based learning framework (V2W-BERT) in this paper. By using ideas from natural language processing, link prediction and transfer learning, our method outperforms previous approaches. We achieve up to 97% prediction accuracy for randomly partitioned data and up to 94% prediction accuracy in temporally partitioned data.
  arXiv  Detail & Related papers  (2021-02-23T05:16:57Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)
• Automated Retrieval of ATT&CK Tactics and Techniques for Cyber Threat Reports [5.789368942487406]
  We evaluate several classification approaches to automatically retrieve Tactics, Techniques and Procedures from unstructured text. We present rcATT, a tool built on top of our findings and freely distributed to the security community to support cyber threat report automated analysis.
  arXiv  Detail & Related papers  (2020-04-29T16:45:14Z)

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.