Common Investigation Process Model for Internet of Things Forensics
- URL: http://arxiv.org/abs/2108.05576v1
- Date: Thu, 12 Aug 2021 07:49:05 GMT
- Title: Common Investigation Process Model for Internet of Things Forensics
- Authors: Muhammed Ahmed Saleh, Siti Hajar Othman, Arafat Al-Dhaqm, Mahmoud
Ahmad Al-Khasawneh
- Abstract summary: Internet of Things Forensics (IoTFs) is a new discipline in digital forensics science used in the detection, acquisition, preservation, rebuilding, analyzing, and the presentation of evidence from IoT environments.
This paper aims to propose a common investigation processes for IoTFs using the metamodeling method called Common Investigation Process Model (CIPM) for IoTFs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Internet of Things Forensics (IoTFs) is a new discipline in digital forensics
science used in the detection, acquisition, preservation, rebuilding,
analyzing, and the presentation of evidence from IoT environments. IoTFs
discipline still suffers from several issues and challenges that have in the
recent past been documented. For example, heterogeneity of IoT infrastructures
has mainly been a key challenge. The heterogeneity of the IoT infrastructures
makes the IoTFs very complex, and ambiguous among various forensic domain. This
paper aims to propose a common investigation processes for IoTFs using the
metamodeling method called Common Investigation Process Model (CIPM) for IoTFs.
The proposed CIPM consists of four common investigation processes: i)
preparation process, ii) collection process, iii) analysis process and iv)
final report process. The proposed CIPM can assist IoTFs users to facilitate,
manage, and organize the investigation tasks.
Related papers
- Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries [51.72836644350993]
Multimodal Pretraining DEL-Fusion model (MPDF)
We develop pretraining tasks applying contrastive objectives between different compound representations and their text descriptions.
We propose a novel DEL-fusion framework that amalgamates compound information at the atomic, submolecular, and molecular levels.
arXiv Detail & Related papers (2024-09-07T17:32:21Z) - Survey and Analysis of IoT Operating Systems: A Comparative Study on the Effectiveness and Acquisition Time of Open Source Digital Forensics Tools [1.0968343822308813]
The main goal of this research project is to evaluate the effectiveness and speed of open-source forensic tools for digital evidence collecting from various Internet-of-Things (IoT) devices.
The project will create and configure many IoT environments, across popular IoT operating systems, and run common forensics tasks in order to accomplish this goal.
arXiv Detail & Related papers (2024-07-01T17:06:32Z) - From Internet of Things Data to Business Processes: Challenges and a Framework [2.9799866120078935]
The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare.
This work proposes a framework to perform a set of structured steps to convert low-level IoT sensor data into higher-level process events.
arXiv Detail & Related papers (2024-05-14T12:07:07Z) - Towards Multi-Objective High-Dimensional Feature Selection via
Evolutionary Multitasking [63.91518180604101]
This paper develops a novel EMT framework for high-dimensional feature selection problems, namely MO-FSEMT.
A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions.
arXiv Detail & Related papers (2024-01-03T06:34:39Z) - MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world.
sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments.
Existing efforts are often specialized to a single sensory modality or prediction task.
This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
arXiv Detail & Related papers (2023-11-10T18:13:08Z) - IoTScent: Enhancing Forensic Capabilities in Internet of Things Gateways [45.44831696628473]
This paper presents IoTScent, an open-source forensic tool that enables IoT gateways and Home Automation platforms to perform IoT traffic capture and analysis.
IoTScent is specifically designed to operate over IEEE5.4-based traffic, which is the basis for many IoT-specific protocols such as Zigbee, 6LoWPAN and Thread.
This work provides a comprehensive description of the IoTScent tool, including a practical use case that demonstrates the use of the tool to perform device identification from Zigbee traffic.
arXiv Detail & Related papers (2023-10-05T09:10:05Z) - Universal Information Extraction as Unified Semantic Matching [54.19974454019611]
We decouple information extraction into two abilities, structuring and conceptualizing, which are shared by different tasks and schemas.
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching framework.
In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand.
arXiv Detail & Related papers (2023-01-09T11:51:31Z) - NAS-FAS: Static-Dynamic Central Difference Network Search for Face
Anti-Spoofing [94.89405915373857]
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems.
Existing methods rely on expert-designed networks, which may lead to a sub-optimal solution for task FAS.
Here we propose the first FAS method based on neural search (NAS), called FAS-FAS, to discover the well-suited task-aware networks.
arXiv Detail & Related papers (2020-11-03T23:34:40Z) - Agile Approach for IT Forensics Management [0.0]
This paper presents the novel flower model, which uses agile methods and forms a new forensic management approach.
In the forensic investigation of such attacks, big data problems have to be solved due to the amount of data that needs to be analyzed.
arXiv Detail & Related papers (2020-07-08T13:48:50Z) - Automatic Business Process Structure Discovery using Ordered Neurons
LSTM: A Preliminary Study [6.6599132213053185]
We propose to retrieve latent semantic hierarchical structure present in business process documents by building a neural network.
We tested the proposed approach on data set of Process Description Documents (PDD) from our practical Robotic Process Automation (RPA) projects.
arXiv Detail & Related papers (2020-01-05T14:19:11Z)
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.