Ontology-based Privacy Preserving Digital Forensics Framework

Wan, Xuejiao; He, Jingrui; Na, Huang; Mai, Yonghao

doi:10.14257/ijsia.2015.9.4.07

Cited by 5 publications

(2 citation statements)

References 9 publications

(9 reference statements)

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“…The lowest accuracy was when Naive Bayes reached the applied algorithm. Furthermore, based on this work, the finding shows that the proposed ML-based end-to-end is the best parameter at each node in the decision tree made from randomly selected numbers in feature selection [25]. In other words, this classifier operates by constructing decision trees at training time and producing the class that is a mode of the types.…”

Section: Resultsmentioning

confidence: 85%

Security Privacy Process Involvement in Cloud Security for Data Preservation against Data Malicious Activity

K Prabu

2023

IJRITCC

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Cloud data sent from the person is attacked, leading to data hacking. Data classification can be made by malware detection, leading to the data warehouse technique and data storage. The cloud data from a particular internet protocol address cannot be hacked. Only random cloud data is hacked. Even though this leads to some illegal issues. The method of managing the cloud data and maintaining the factor to hack illegal cloud data has been proposed. The method of malware detection and ML-based end-to-end malware detection are used in calculating the time efficiency. The malware detection and defence method has been introduced for managing the data tracking and the system's formation to hack unwanted data. The time efficiency calculation for the data transmitted in the network has been enabled for the cloud data sent and received. The data from each router makes the data store 12% of the unwanted compared to the original messages. The factor for managing the individual aspect to produce the data is 30% of the database. This will contain 20% of the data in formulating the cloud storage system, which makes the data classifications. 4% of redundant data from the database has been enveloped for the data classifications. Meanwhile, the data attack can be evaluated using the malware detector and also manages classification method for evaluation of data and formation of the system to produce data from the appearance of Secure data clouds.

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Section: Resultsmentioning

confidence: 85%

Security Privacy Process Involvement in Cloud Security for Data Preservation against Data Malicious Activity

K Prabu

2023

IJRITCC

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“…The authors on [38] proposed a solution based on the theory of ontology to preserve the privacy in the area of digital forensics by abstracting the privacy attributes in digital forensics scenarios.…”

Section: Related Workmentioning

confidence: 99%

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

AlKhanafseh,

Surakhi

2024

Preprint

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The adoption of technology and digital tools across various user groups has brought about a heightened risk of cyber-attacks targeting devices and shared data. These elevated levels of cybercrime necessitate thorough investigations to address victims’ inquiries about the nature, causes, affected data, and impacted resources of the attack. Digital forensics serves as a crucial means to answer these queries. Additionally, it enables the formulation of strategies and delineation of roles essential for fortifying future defenses against such attacks. Within digital forensics, digital evidence holds pivotal importance in the investigative process as it serves as a cornerstone in legal proceedings to identify perpetrators. Numerous research endeavors focus on devising solutions to safeguard evidence, including centralized storage centers or cloud computing. These efforts aim to create frameworks integrating diverse security levels for sharing and preserving evidence effectively. In this paper, a new framework is proposed that seeks to amalgamate novel technologies with forensic practices to address challenges in the preservation phase of digital forensics. Strategies include segmenting evidence into smaller components, leveraging steganography to support preservation, and employing blockchain technology to ensure the integrity of collected evidence.

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Advances in Digital Forensics Frameworks and Tools

Abulaish

Haldar

2020

Cyber Warfare and Terrorism

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Digital forensics science is a well-known initiative to unearth computer-assisted crimes. The thriving criminal activities using digital media have changed the typical definition of a traditional crime. Meanwhile, the means and targets of criminal activities have been transformed in a broader context due to the diverse nature of offenses associated with the multiple crime categories, affecting the way of investigations as well. In order to withstand the difficulties caused due to the crime complexity, forensics investigation frameworks are being tuned to adjust with the nature and earnestness of the felonies being committed. This article presents an in-depth comparative survey of fourteen popular and most cited digital forensics process models and various forensics tools associated with different phases of these models. The relationships among these forensics process models and their evolutions are analyzed and a graph-theoretic approach is presented to rank the existing process models to facilitate investigators in selecting an appropriate model for their investigation tasks.

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Ontology-based Privacy Preserving Digital Forensics Framework

Abstract: Abstract

Cited by 5 publications

References 9 publications

Security Privacy Process Involvement in Cloud Security for Data Preservation against Data Malicious Activity

Security Privacy Process Involvement in Cloud Security for Data Preservation against Data Malicious Activity

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

Advances in Digital Forensics Frameworks and Tools

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