Advancing Cyber Incident Timeline Analysis Through Rule Based AI and Large Language Models

• URL: http://arxiv.org/abs/2409.02572v3
• Date: Wed, 25 Sep 2024 06:50:29 GMT
• Title: Advancing Cyber Incident Timeline Analysis Through Rule Based AI and Large Language Models
• Authors: Fatma Yasmine Loumachi, Mohamed Chahine Ghanem,
• Abstract summary: This paper introduces a novel framework, GenDFIR, which combines Rule-Based Artificial Intelligence (R-BAI) algorithms with Large Language Models (LLMs) to enhance and automate the Timeline Analysis process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timeline Analysis (TA) plays a crucial role in Timeline Forensics (TF) within the field of Digital Forensics (DF). It focuses on examining and analyzing time-based digital artefacts, such as timestamps derived from event logs, file metadata, and other relevant data, to correlate events linked to cyber incidents and reconstruct their chronological sequence. Traditional tools often struggle to efficiently handle the large volume and variety of data generated during DF investigations and Incident Response (IR) processes. This paper introduces a novel framework, GenDFIR, which combines Rule-Based Artificial Intelligence (R-BAI) algorithms with Large Language Models (LLMs) to enhance and automate the TA process. The proposed approach consists of two key stages: (1) R-BAI is used to identify and select anomalous digital artefacts based on predefined rules. (2) The selected artefacts are then transformed into embeddings for processing by an LLM with the assistance of a Retrieval-Augmented Generation (RAG) agent. The LLM uses its capabilities to perform automated TA on the artefacts and predict potential incident outcomes. To validate the framework, we evaluated its performance, efficiency, and reliability. Several metrics were applied to simulated cyber incident scenarios, which were presented as forensic case documents. Our findings demonstrate the significant potential of integrating R-BAI and LLMs for TA. This innovative approach underscores the power of Generative AI (GenAI), particularly LLMs, and opens up new possibilities for advanced threat detection and incident reconstruction, marking a significant advancement in the field.

Related papers

• See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers [23.701716999879636]
  Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data. We introduce a pioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA) to enhance both the detection and interpretation of anomalies.
  arXiv  Detail & Related papers  (2024-11-04T10:28:41Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era [59.279784235147254]
  This survey provides an in-depth summary of the latest approaches that are based on recurrent models for sequential data processing. The emerging picture suggests that there is room for thinking of novel routes, constituted by learning algorithms which depart from the standard Backpropagation Through Time.
  arXiv  Detail & Related papers  (2024-06-13T12:51:22Z)
• Can Foundational Large Language Models Assist with Conducting Pharmaceuticals Manufacturing Investigations? [0.0]
  We focus on a specific use case, pharmaceutical manufacturing investigations. We propose that leveraging historical records of manufacturing incidents and deviations can be beneficial for addressing and closing new cases. We show that semantic search on vector embedding of deviation descriptions can be used to identify similar records.
  arXiv  Detail & Related papers  (2024-04-24T00:56:22Z)
• System for systematic literature review using multiple AI agents: Concept and an empirical evaluation [5.194208843843004]
  We introduce a novel multi-AI agent model designed to fully automate the process of conducting Systematic Literature Reviews. The model operates through a user-friendly interface where researchers input their topic. It generates a search string used to retrieve relevant academic papers. The model then autonomously summarizes the abstracts of these papers.
  arXiv  Detail & Related papers  (2024-03-13T10:27:52Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Position: What Can Large Language Models Tell Us about Time Series Analysis [69.70906014827547]
  We argue that current large language models (LLMs) have the potential to revolutionize time series analysis. Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering.
  arXiv  Detail & Related papers  (2024-02-05T04:17:49Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks. Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete. This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications [5.465142671132731]
  Adversarial testing of large language models (LLMs) is crucial for their safe and responsible deployment. We introduce a novel approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. We call it AI-assisted Red-Teaming (AART) - an automated alternative to current manual red-teaming efforts.
  arXiv  Detail & Related papers  (2023-11-14T23:28:23Z)
• A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response [0.0]
  The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.
  arXiv  Detail & Related papers  (2023-09-13T16:23:53Z)

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.