Digital Forensics Challenges to Big Data in the Cloud

Abstract

As a new research area, Digital Forensics is a subject in a rapid development society. Cyber security for Big Data in the Cloud is getting attention more than ever. Computing breach requires digital forensics to seize the digital evidence to locate who done it and what has been done maliciously and possible risk/damage assessing what loss could leads to. In particular, for Big Data attack cases, Digital Forensics has been facing even more challenge than original digital breach investigations. Nowadays, Big Data due to its characteristics of three "V"s (Volume, Velocity, and Variety), they are either synchronized with Cloud (Such as smart phone) or stored on the Cloud, in order to sort out the storage capacity etc. problems, which made Digital Forensics investigation even more difficult. The Big Data-Digital Forensics issue for Cloud is difficult due to some issues. One of them is physically identify specific wanted device. Data are distributed in the cloud, customer or the digital forensics practitioner cannot have a fully access control like the traditional investigation does. The Smart City technique is making use of ICT (information communications technology) to collecting, detecting, analysing and integrating the key information data of core systems in running the cities. Meantime, the control is making intelligent responses to different requirements that include daily livelihood, PII (Personally identifiable information) security, environmental protection, public safety, industrial and commercial activities and city services. The Smart City data are Big Data, collected and gathered by the IoT (Internet of Things). This paper has summerised our review on the trends of Digital Forensics served for Big Data. The evidence acquisition challenge is discussed. A case study of a Smart City project with the IoT collected services Big data which are stored at the cloud computing environment is represented. The techniques can be generalised to other Big Data in the Cloud environment.

The complexity of internet of things forensics: A state-of-the-art review

“…It is noted by [1] that this large data generated presents digital forensics expert the difficulty of collecting and extracting evidential data in a smooth manner. A research by [15] summarises the review on digital forensics trends used for Big Data and the challenges encountered in the acquisition of evidence. A Smart City project is used as a case study where IoT services collect Big Data and store it in the cloud.…”

“…However, the laws are only limited to EU regions. There is no physical access of the storage facilities and that digital forensics investigators rely heavily on the Cloud Service Providers (CSP) for cooperation on the retrieval of evidence, this was highlighted by [15]. The cross-border technicalities that make it hard to establish a chain of custody as required by law have been highlighted as a challenge to IoT forensics.…”

Use of Big Data Tools and Industrial Internet of Things: An Overview

“…A contextual investigation of a smart city venture with IoT administrations gathering big data which are put away in the cloud processing condition is presented. e strategies can be summed up to other big data in the cloud environment [43]. A fault prediction technique dependent on industrial big data is presented, which legitimately exhumes the connection between the data, for example, the status as well as sound data, and the equipment faults by machine learning techniques [44].…”

“…Figure 9 shows the article types and percentages of publication in the Springer library. [9] Big data analytics tool based on statistical process monitoring for smart manufacturing 2 [11] Multimedia big data computation and applications of IoT 3 [12] IoT, big data, and HPC-based smart flood management framework 4 [15] Big data analytics for manufacturing processes 5 [17] An algorithmic implementation of entropic ternary reduct soft sentiment set using soft computing technique on big data sentiment analysis for optimal selection of a decision based on real-time update in online reviews 6 [18] Architecture for Cognitive IoT and big data 7 [20] Challenges and opportunities for publishing IIoT data in manufacturing 8 [21] A comprehensive review of big data analytics throughout product life cycle to support sustainable smart manufacturing 9 [22] Role of big data analytics in IIoT 10 [23] Big data and natural environment 11 [30] Intelligent manufacturing production line data monitoring system for IIoT 12 [31] A secure and efficient data sharing scheme based on blockchain in IIoT 13 [32] Data management techniques for IoT 14 [33] Scalable data pipeline architecture to support the IIoT 15 [34] Industry 4.0-based process data analytics platform 16 [35] Optimization of IIoT data processing latency 17 [36] Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case 18 [37] Framework of big data for sustainable and smart additive manufacturing 19 [38] Feature engineering in big data analytics for IoT-enabled smart manufacturing 20 [39] An architecture for aggregating information from distributed data nodes for IIoT 21 [40] Application of big data analysis technique on high-velocity airblast atomization 22 [23] Interactive data exploration as a service for the smart factory 23 [41] Smart city services using machine learning, IoT, and big data 24 [43] Digital forensics challenges to big data in the cloud 25 [44] On fault prediction based on industrial big data 26 [45] Apache spark-based distributed self-organizing map algorithm for sensor data analysis 27…”

A Hyperconnected Smart City Framework

“…We offer the privacy and security recommendations that can help save the cities from those attacks or take appropriate measures for mitigation (Luo et. al., 2019;Liu, Nakauchi, & Shoji, 2018;Zhang et al, 2015;Feng, & Zhao, 2018;Kuan et al, 2017). Since numerous relationships exist within a relationship model as illustrated in Figure 11.…”