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******Introduction and Motivation******
Big data paradigm is present in every aspect of cybercrime investigations. Personal computers and laptops have had a steady increase in storage over the years, where it has become common with terabytes in hard drive space. This increase in storage space has a major consequence on timely results delivery for digital investigations, as it might take up to several hours only to image the hard drive, in addition to file carving and normal forensic analysis. This investigative model only considers personal devices we use daily. Taking high- and mid-class servers into the equation will increase the problem manifold, as they have hard drive storage already starting from tens and hundreds of terabytes. Further on, popularity of mobile phones have increased. They have transformed from simple end-user communication terminals, into a powerful and resourceful tool capable of massive parallel computing. They are also storing a variety of log data from various sources such as GPS and other sensors in addition to user's photos, music and documents. As result, they are capable of storing hundreds of gigabytes of personal and sensitive information. There is a strong need for advanced data analytic to aid cybercrime investigations on a large-scale data, which requires novel approaches for more efficient and effective automated analysis.
The website of the Special Issue: https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/big-data-analytics-and-artificial-intelligence-for-cyber-cri
******Background******
Cybercrimes can be considered as a major challenge as they are not gathered in a finite set of local crime scenes. Possible traces of evidences are spread across multiple systems, with multiple victims, and cross more jurisdictions than ever before. It is difficult to have human experts to efficiently correlate data from different crimes and crime scenes. A strong demand for advanced data analytic has expanded and becoming disruptive. Another challenge is varieties of file formats, compression, encryption, file systems, etc.
Over last few years authors have been raising an importance of advanced data analytics for digital forensics in their research. Such that digital forensics is already considered to be a big data challenge and therefore require complete rethinking of principles and workflow (Franke 2008). The problem that investigators face now is that their tools - developed to analyse early 2000's technology - are no longer sufficient. For example, most of the computers in 1990th had storage equal to hundreds of MBytes. This means that most of the files could be reviewed by a single person in a timely manner. In 2018, smartphones have 128 GBytes storage, while computers and laptops hit 2-4 TBytes disk storage level already. Such capacity makes manual investigations simply infeasible. An idiom to this is the 'needle in the haystack' referring to a need to filter out the noise and to discover patterns in large heaps of data to uncover tiny pieces of evidence. However, the same method finding the needle in one haystack, does not necessarily work for another haystack. Therefore, there is a need for research for new ways of thinking and processing methods. For example, research into data reduction techniques, data mining and intelligent analysis (Quick et al. 2014).
******The Scope of the Special Issue******
The objective of the special issue is to attract research of novel methods, techniques and data analytic approaches, previously unpublished or substantially improved previous contributions (with at least 60% of new material). Authors of papers that fit these criteria will be invited to submit their contributions to the Special Issue. Moreover, following the positive feedback and great interest last year, authors of nominated best papers will be invited to submit their extended contributions from the 2nd International Workshop on Big Data Analytic for Cybercrime Investigation and Prevention 2018.
******Selection and Evaluation Criteria******
1. Relevance to the cybercrime investigation and prevention
2. Applicability in large-scale digital evidences analytics
3. Research novelty and impact of the submitted work
4. Readability and technical quality
******Research Topics******
1. New development in data-driven methods
- Novel datasets
- New data formats
- Digital forensics data simulation
- Anonymised case data
- New data formats and taxonomies
2. Novel computational intelligence methods and improvement of existing algorithms
- Machine learning-aided analysis
- Graph-based detection
- Topic modelling
- Improvements of existing methods
- Decision support systems
3. Application areas and cross-domain information exchange
- Cyber threats intelligence
- Network forensics readiness
- Malware analysis & detection
- Emails mining & authorship identification
- Social network mining
- Events correlations
- Access logs analysis
- Mobile forensics
- Fraud detection
- Database forensics
- Internet of things forensics
- Blockchain technologies
- Industrial systems
4. Platforms, architecture and infrastructure for efficient data analytics
- Secure collaborative platforms
- Distributed storage and processing
- Technologies for data streams
- Hardware and software architectures for large-scale data
******Submission Guidelines******
The submission website for this journal is located at: http://www.evise.com/evise/jrnl/FGCS
"Special Issue on Big Data Analytics and Artificial Intelligence for Cyber Crime Investigation and Prevention" has the following identifier BDAAICCIP2019
******Important Dates******
Final submission date: September 20, 2019
Final acceptance deadline: November 15, 2019
******Guest Editors******
Andrii Shalaginov, [email protected], Norwegian University of Science and Technology, Gjøvik, Norway
Jan William Johnsen, [email protected], Norwegian University of Science and Technology, Gjøvik, Norway
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