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DBLP
Article . 2022
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HoneyNetCloud Investigation Model, A Preventive Process Model for IoT Forensics

Authors: Jayakrishnan Anilakkad Raman; Vasanthi Varadharajan;

HoneyNetCloud Investigation Model, A Preventive Process Model for IoT Forensics

Abstract

With the pervasive usage of sensing systems and IoT things, the importance of security has increased. Attempts towards breaching IoT security systems by attackers are on upsurge. Many intrusions in embedded systems, sensing equipment and IoT things have occurred in the past. Though there are cyber security tools like Antivirus, Intrusion detection and prevention systems available for securing the digital devices and its networks. However, a forensic methodology to be followed for the analysis and investigation to detect origin cause of network incidents is lacking. This paper derives a comprehensive preventive cyber forensic process model with honeypots for the digital IoT investigation process which is formal, that can assist in the court of law in defining the reliability of the investigative process. One year data of various attacks to the IoT network has been recorded by the honeypots for this study. The newly derived model HIM has been validated using various methods and instead of converging on a particular aspect of investigation, it details the entire lifecycle of IoT forensic investigation. The model is targeted to address the forensic analysts’ requirements and the need of legal fraternity for a forensic model. The process model follows a preventive method which reduce further attacks on network.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
bronze