Evidence map›Paper›PMID 40404782›Full record

ArticleScientific reports2025

Digital forensics and incident response management model for IoT based agriculture.

Santoshi Rudrakar, Parag Rughani, Lakshminarayana Sadineni

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Santoshi RudrakarNational Forensic Sciences University, Gandhinagar, India.
Parag RughaniNational Forensic Sciences University, Gandhinagar, India. parag.rughani@nfsu.ac.in.
Lakshminarayana SadineniManipal University Jaipur, Jaipur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet of Things (IoT) has been revolutionizing the agricultural industry by providing farmers with unprecedented opportunities to monitor and control their crops, livestock, and farm equipment in real-time, which is named as IoT based Agriculture (Ag-IoT). Ag-IoT relies on the use of communication technology, internet, and other wireless technologies which makes it prone to various cyber attack and cyber crimes. To address the growing security and forensic challenges in Ag-IoT, we propose a Digital Forensics and Incident Response Management Model (DFIRMM). The proposed model focuses on the identification, analysis, and mitigation of security incidents, along with support for the investigation of digital forensics tailored to the unique requirements of Ag-IoT. The proposed model is validated through a case study on MQTT enabled smart agriculture network with machine learning based analysis. We believe this proposed model will redefine how security incidents are handled in smart agriculture industries and impact their growth.

Indexed as

Cyber attacks and cyber crimeCyber securityDigital forensics and incident response management modelIoT based agricultureMQTT attack

Identifiers

PMID40404782
PMCPMC12098995

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.