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A Brief Review of Network Forensics Process Models and a Proposed Systematic Model for Investigation

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI 2022)

Abstract

Network forensics is a branch of Digital Forensics concerned with analysing the network traffic to see if any anomalies are present that may indicate an attack or could lead to one. The goal is to figure out what kind of attack it is by capturing the details, store them in a forensically sound manner, analyse, and then present them in some visual form. A model based on traceability and scenarios, with proven literature and justification is desired. This study offers a professional digital framework in which the investigative process model enhances the systematic tracking of offenders. Cyber fraud and digital crimes are on the rise, and unfortunately less than two per cent is the conviction rate worldwide. Continuous and scientific research in this area is crucial to ensure safe and secure internet usage especially for money transfers and confidential personal communication. This paper examines the essential development phases of a Network forensics investigation model, and compares different network and digital forensic methods, and also offers a systematic model of a digital forensic model for cybercrime investigation. The survey also includes classifications based on infiltration detection systems, trace backs, distribution models, and attack maps. The aim of this study is to facilitate the digital forensic process and identify improvised practices. The Systematic Network Forensic Investigation model (SNFIM) aims to establish appropriate policies and procedures for practitioners and organizations.

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Thomas, M., Meshram, B. (2023). A Brief Review of Network Forensics Process Models and a Proposed Systematic Model for Investigation. In: Hemanth, J., Pelusi, D., Chen, J.IZ. (eds) Intelligent Cyber Physical Systems and Internet of Things. ICoICI 2022. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-031-18497-0_45

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