Big data and digital forensics

Adedayo, Oluwasola Mary

doi:10.1109/icccf.2016.7740422

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…Zawoad and Hasan [31] proposed a conceptual model for big data forensics based on HDFS and cloud architecture and reduced the entire forensic time by eliminating redundant data from incoming streaming data. Adedayo et al [32] presented new challenges and opportunities for forensic analysis of large-scale data such as identification, collection, organization, preservation, and presentation. Thaneker et al [33] proposed a forensic framework on largescale data that uses Hadoop for storing and managing forensic evidence, and used Autopsy, an open-source forensic tool, for file carving, data carving, and keyword searching.…”

Section: Big Data Forensicsmentioning

confidence: 99%

Large-scale digital forensic investigation for Windows registry on Apache Spark

Lee

Kwon

2022

PLoS ONE

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In this study, we investigate large-scale digital forensic investigation on Apache Spark using a Windows registry. Because the Windows registry depends on the system on which it operates, the existing forensic methods on the Windows registry have been targeted on the Windows registry in a single system. However, it is a critical issue to analyze large-scale registry data collected from several Windows systems because it allows us to detect suspiciously changed data by comparing the Windows registry in multiple systems. To this end, we devise distributed algorithms to analyze large-scale registry data collected from multiple Windows systems on the Apache Spark framework. First, we define three main scenarios in which we classify the existing registry forensic studies into them. Second, we propose an algorithm to load the Windows registry into the Hadoop distributed file system (HDFS) for subsequent forensics. Third, we propose a distributed algorithm for each defined forensic scenario using Apache Spark operations. Through extensive experiments using eight nodes in an actual distributed environment, we demonstrate that the proposed method can perform forensics efficiently on large-scale registry data. Specifically, we perform forensics on 1.52 GB of Windows registry data collected from four computers and show that the proposed algorithms can reduce the processing time by up to approximately 3.31 times, as we increase the number of CPUs from 1 to 8 and the number of worker nodes from 2 to 8. Because the distributed algorithms on Apache Spark require the inherent network and MapReduce overheads, this improvement of the processing performance verifies the efficiency and scalability of the proposed algorithms.

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Section: Big Data Forensicsmentioning

confidence: 99%

Large-scale digital forensic investigation for Windows registry on Apache Spark

Lee

Kwon

2022

PLoS ONE

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“…Moreover, with the help of computational intelligence and AI, forensic investigators can employ digital forensic methods more efficiently while ensuring the credibility and reliability of the results [39]. AI also helps in handling large datasets, while collecting digital evidence for forensics [40]. Thus, it can be established that AI has the capability to dynamically transform the way digital forensic works while increasing the accuracy of the results and lowering the time needed to extract useful digital evidence.…”

Section: Author Detailsmentioning

confidence: 99%

Digital Forensics in Cyber Security—Recent Trends, Threats, and Opportunities

Alghamdi¹

2021

Cybersecurity Threats With New Perspectives

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The rapid technological advancement has led the entire world to shift towards digital domain. However, this transition has also result in the emergence of cybercrimes and security breach incidents that threatens the privacy and security of the users. Therefore, this chapter aimed at examining the use of digital forensics in countering cybercrimes, which has been a critical breakthrough in cybersecurity. The chapter has analyzed the most recent trends in digital forensics, which include cloud forensics, social media forensics, and IoT forensics. These technologies are helping the cybersecurity professionals to use the digital traces left by the data storage and processing to keep data safe, while identifying the cybercriminals. However, the research has also observed specific threats to digital forensics, which include technical, operational and personnel-related challenges. The high complexity of these systems, large volume of data, chain of custody, the integrity of personnel, and the validity and accuracy of digital forensics are major threats to its large-scale use. Nevertheless, the chapter has also observed the use of USB forensics, intrusion detection and artificial intelligence as major opportunities for digital forensics that can make the processes easier, efficient, and safe.

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“…Digitalization has begun to be a part of our daily life after the maturation and spread of emerging technologies. One of the main reasons for digitalization is to reduce or even eliminate the need for human power [1]. With the widespread use of the Industry 4.0 approach, it is predicted that 30% of today's jobs, including white collar positions in all departments, will be completed by robots [2].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin Case Study on Automotive Production Line

Mendı

2022

Sensors

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The manufacturing sector is one of the areas where the advantages of digital twin technology can benefit mostly. The product development, including its software, electronics, mechanics, and physical behavior, is included in the digital twin of the product. Furthermore, simultaneous data capturing from the sensors and data processing are also available in the digital twin. This enables each phase of the development cycle to be simulated, processed, and validated to discover the potential problems before the production of real components. In this study, the use of digital twin technology in the commercial production phase of the automotive production line with a case study is introduced. This study is one of the most comprehensive studies in the literature related to automotive production; therefore, it puts forth the power of using digital twin technology in that area. As the result of this case study, a 6.01% increase in the commercial production line efficiency and an 87.56% gain for downtime are achieved.

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Big data and digital forensics

Cited by 13 publications

References 22 publications

Large-scale digital forensic investigation for Windows registry on Apache Spark

Large-scale digital forensic investigation for Windows registry on Apache Spark

Digital Forensics in Cyber Security—Recent Trends, Threats, and Opportunities

A Digital Twin Case Study on Automotive Production Line

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