Advancing Cyber Incident Timeline Analysis Through Rule Based AI and Large Language Models
<|reference_start|>Advancing Cyber Incident Timeline Analysis Through Rule Based AI and Large Language Models: 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.<|reference_end|>
@article{loumachi2024advancing,
    title={Advancing Cyber Incident Timeline Analysis Through Rule Based AI and
  Large Language Models},
    author={Fatma Yasmine Loumachi and Mohamed Chahine Ghanem},
    journal={arXiv preprint arXiv:2409.02572},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02572},
    primaryClass={cs.CR cs.AI cs.ET cs.LG}
}