Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency

• URL: http://arxiv.org/abs/2402.19366v3
• Date: Fri, 31 Jan 2025 08:27:13 GMT
• Title: Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
• Authors: Akila Wickramasekara, Frank Breitinger, Mark Scanlon,
• Abstract summary: The ever-increasing workload of digital forensic labs raises concerns about law enforcement's ability to conduct both cyber-related and non-cyber-related investigations promptly. This article explores the potential and usefulness of integrating Large Language Models into digital forensic investigations to address challenges such as bias, explainability, censorship, resource-intensive infrastructure, and ethical and legal considerations.
• Score: 0.1433758865948252
• License:
• Abstract: The ever-increasing workload of digital forensic labs raises concerns about law enforcement's ability to conduct both cyber-related and non-cyber-related investigations promptly. Consequently, this article explores the potential and usefulness of integrating Large Language Models (LLMs) into digital forensic investigations to address challenges such as bias, explainability, censorship, resource-intensive infrastructure, and ethical and legal considerations. A comprehensive literature review is carried out, encompassing existing digital forensic models, tools, LLMs, deep learning techniques, and the use of LLMs in investigations. The review identifies current challenges within existing digital forensic processes and explores both the obstacles and the possibilities of incorporating LLMs. In conclusion, the study states that the adoption of LLMs in digital forensics, with appropriate constraints, has the potential to improve investigation efficiency, improve traceability, and alleviate the technical and judicial barriers faced by law enforcement entities.

Related papers

• Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review [0.0]
  Machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL approaches used in these fields showcasing their advantages and possibilities. It covers a range of AI techniques used in spotting intrusions in systems and classifying malware to prevent cybersecurity attacks, detect anomalies and enhance resilience.
  arXiv  Detail & Related papers  (2024-12-24T17:22:51Z)
• LegalAgentBench: Evaluating LLM Agents in Legal Domain [53.70993264644004]
  LegalAgentBench is a benchmark specifically designed to evaluate LLM Agents in the Chinese legal domain. LegalAgentBench includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.
  arXiv  Detail & Related papers  (2024-12-23T04:02:46Z)
• Evaluating Copyright Takedown Methods for Language Models [100.38129820325497]
  Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material. This paper introduces the first evaluation of the feasibility and side effects of copyright takedowns for LMs. We examine several strategies, including adding system prompts, decoding-time filtering interventions, and unlearning approaches.
  arXiv  Detail & Related papers  (2024-06-26T18:09:46Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey [1.0770079992809338]
  The capabilities of Large Language Models (LLMs) are increasingly demonstrating unique roles in the legal sector. This survey delves into the synergy between LLMs and the legal system, such as their applications in tasks like legal text comprehension, case retrieval, and analysis. The survey showcases the latest advancements in fine-tuned legal LLMs tailored for various legal systems, along with legal datasets available for fine-tuning LLMs in various languages.
  arXiv  Detail & Related papers  (2024-04-01T08:35:56Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• 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)
• A Short Survey of Viewing Large Language Models in Legal Aspect [0.0]
  Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. The integration of LLMs into the legal field has also raised several legal problems, including privacy concerns, bias, and explainability.
  arXiv  Detail & Related papers  (2023-03-16T08:01:22Z)
• SoK: Blockchain Solutions for Forensics [8.185918509343818]
  This paper provides an overview and classification of the available blockchain-based digital forensic tools. We also offer an analysis of the various benefits and challenges of the symbiotic relationship between blockchain technology and the current digital forensics approaches.
  arXiv  Detail & Related papers  (2020-05-26T11:43:04Z)

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.