Challenges of Data Provenance for Cloud Forensic Investigations

Katilu, Victoria M.; Franqueira, Virginia N. L.; Angelopoulou, Olga

doi:10.1109/ares.2015.54

Cited by 6 publications

(8 citation statements)

References 30 publications

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“…It also includes information of the nodes responsible for retransmitting information through intermediaries. In [24] a method was developed that integrate provenance data on two levels; a low-level through information cascades, and a high-level through similaritybased clustering.…”

Section: A Social Mediamentioning

confidence: 99%

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

Alkhalil¹,

Ramadan²,

Ahmad³

2019

IJACSA

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Big data systems are being increasingly adopted by the enterprises exploiting big data applications to manage datadriven process, practices, and systems in an enterprise wide context. Specifically, big data systems and their underlying applications empower enterprises with analytical decision making (e.g., recommender/decision support systems) to optimize organizational productivity, competitiveness, and growth. Despite these benefits, big data applications face some challenges that include but not limited to security and privacy, authenticity, and reliability of critical data that may result in propagation of false information across systems. Data provenance as an approach and enabling mechanism (to identify the origin, manage the creation, and track the propagation of information etc.) can be a solution to above mentioned challenges for data management in an enterprise context. Data provenance solution(s) can help stakeholders and enterprises to assess the quality of data along with authenticity, reliability, and trust of information on the basis of identity, reproducibility and integrity of data. Considering the wide spread adoption of big data applications and the needs for data provenance, this paper focuses on (i) analyzing state-of-the-art for holistic presentation of provenance in big-data applications (ii) proposing a bioinspired approach with underlying algorithm that exploits human thinking approach to support data provenance in Wireless Sensor Networks (WSNs). The proposed 'Think-and-Share Optimization' (TaSO) algorithms modularizes and automates data provenance in WSNs that are deployed and operated in enterprises. Evaluation of TaSO algorithm demonstrates its efficiency in terms of connectivity, closeness to the sink node, coverage, and execution time. The proposed research contextualizes bio-inspired computation to enable and optimize data provenance in WSNs. Future research aims to exploit machine learning techniques (with underlying algorithms) to automate data provenance for big data systems in networked environments.

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Section: A Social Mediamentioning

confidence: 99%

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

Alkhalil¹,

Ramadan²,

Ahmad³

2019

IJACSA

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“…Only methods and techniques that can establish data provenance and describe the automatically generated results using a logically explainable theory are accepted [ 9 , 10 ]. Several data mining techniques lose provenance information while pre-processing and normalizing the data [ 11 , 12 ] As a result, most methods for analyzing social media using data mining techniques are not approved for legal or forensic use because they tend to lose provenance during data pre-processing and processing [ 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Arshad

Abdullah

Alawida

et al. 2022

Sensors

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Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

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“…The popularity of the cloud is associated with its effectiveness and robustness [5] However, the control of data, monitoring and a trusted to CSPs and cloud service administrators. According to the National Institute of Standards and Technology, NIST, defines it as "a model for enabling ubiquitous, convenient, on demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" [6].…”

Section: Cloud Computingmentioning

confidence: 99%

“…Based on experiments conducted using objects {A,B} and {A,C}, {D,E}, {F,G}, {F,H} the solution converges to different figures when [1,2], [2,3], [2,4], [2,5], [2,6] root interval are used. Table 3 shows the convergence and functional values of the experiments …”

Section: Data Sets For Conducted Experimentsmentioning

confidence: 99%

“…The testing experiments have generated five different scenarios for the objects {A,B} and {A,C}, {D,E}, {F,G}, {F,H} and the test values [1,2], [2,3], [2,4], [2,5] and [2,6] respectively. The authors chose to carry out these separate tests for two main reasons: To find the convergence of the roots that are treated as data objects that move within the cloud and to find the functional object that will help to show how closeness the objects can be during data provenance.…”

Section: Concept Evaluationmentioning

confidence: 99%

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An Approach for Estimating Forensic Data Provenance of an Object in the Cloud Environment using One Dimensional Successive Bisection Method

Kebande¹,

Karie²

2016

IJCA

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The decline in traditional forensics and the emergence of cloud forensics has made it hard for digital forensic investigators to conduct digital investigations due to inadaptability of the cloud to digital forensic processes. Additionally, data that resides in the cloud is normally scattered across constantly changing data centers, hosts and virtual instances are easily destroyed as they are created. On the same note it is not easy to seize physical devices where a particular crime has occurred and locating the origin of an object in the cloud can be a challenge when we do not know where the actual data resides. Data provenance provides a solution whereby one can trace data based on the tag as it keeps changing directions in the cloud. However, if the distance between two particular tagged data is taken into account then the source and destination of an object can be located easily which can then enable digital forensic investigators to locate the physical devices. The problem that this paper is addressing is that, there is no easy way of locating data provenance in the cloud environment without estimating the distance between tagged data that moves in the cloud. Based on this, the authors have proposed a mechanism for locating the provenance of an object using successive bisection method. Similar test have been carried using different intervals in an experiment and the results are very promising.

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Challenges of Data Provenance for Cloud Forensic Investigations

Cited by 6 publications

References 30 publications

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

Bio-inspired Think-and-Share Optimization for Big Data Provenance in Wireless Sensor Networks

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

An Approach for Estimating Forensic Data Provenance of an Object in the Cloud Environment using One Dimensional Successive Bisection Method

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