Pith. sign in

REVIEW 1 cited by

Responding to Living-Off-the-Land Tactics using Just-in-Time Memory Forensics (JIT-MF) for Android

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2105.05510 v1 pith:6U62ODIH submitted 2021-05-12 cs.CR

classification cs.CR
keywords jit-mfmemoryforensicsandroiddriversevidenceforensicapps
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Digital investigations of stealthy attacks on Android devices pose particular challenges to incident responders. Whereas consequential late detection demands accurate and comprehensive forensic timelines to reconstruct all malicious activities, reduced forensic footprints with minimal malware involvement, such as when Living-Off-the-Land (LOtL) tactics are adopted, leave investigators little evidence to work with. Volatile memory forensics can be an effective approach since app execution of any form is always bound to leave a trail of evidence in memory, even if perhaps ephemeral. Just-in-Time Memory Forensics (JIT-MF) is a recently proposed technique that describes a framework to process memory forensics on existing stock Android devices, without compromising their security by requiring them to be rooted. Within this framework, JIT-MF drivers are designed to promptly dump in-memory evidence related to app usage or misuse. In this work, we primarily introduce a conceptualized presentation of JIT-MF drivers. Subsequently, through a series of case studies involving the hijacking of widely-used messaging apps, we show that when the target apps are forensically enhanced with JIT-MF drivers, investigators can generate richer forensic timelines to support their investigation, which are on average 26% closer to ground truth.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MoLIFE: Methodology, Technologies, and Challenges for Mobile Live Intelligent Forensics Examination

    cs.CR 2026-07 conditional novelty 5.0 of 10

    A mobile Digital Twin with super-user privileges can replicate a real device's forensic data content at 95-100% similarity, enabling privileged forensic analysis without modifying the physical evidence.

Pith tools