Special Issue "Advanced Big Data Analytics for Cyber and Cyber-Physical Crime Investigations"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (15 January 2020).

Special Issue Editors

Dr. Andrii Shalaginov
E-Mail Website1 Website2
Guest Editor
Norwegian University of Science and Technology, Gjøvik, Norway
Interests: large-scale data analytic; malware detection; intelligent information security; internet of things; smart applications
Special Issues, Collections and Topics in MDPI journals
Mr. Asif Iqbal CISSP, CISA, CISM
E-Mail Website
Guest Editor
KTH Royal Institute of Technology in Stockholm, Sweden
Interests: digital forensic and security investigations; cybercrime investigations; forensic intelligence information security modeling; cyber warfare; IoT; IIoT; SCADA/ICS
Special Issues, Collections and Topics in MDPI journals
Dr. Ambika Shrestha Chitrakar
E-Mail Website
Guest Editor
Norwegian University of Science and Technology, Gjøvik, Norway
Interests: approximate search; data reduction; big data analytics; network security; digital forensics; data science

Special Issue Information

Dear Colleagues,

With the rapid development and integration of new technologies, in today’s connected world, crimes get committed not only in the physical realm, but even more commonly in cyberspace, as well. Despite the development of new methods to prevent cyber-attacks, the amount of actual data affiliated with crimes has increased dramatically. Traditionally, personal computers, powerful servers, and mobile phones have long been considered as the main sources of the data to be processed in a digital investigation. However, the changing nature of our world around us brings previously unseen concepts, such as a variety of smart applications, autonomous systems, and intelligent societal services. All these simplify everyday tasks and facilitate societal needs, while at the same time bringing new vulnerabilities, attack vectors, and previously unknown consequences of attacks to life. The steady increase in digital storage size over the last few decades has now resulted in terabytes of data in use in a single household, not to mention large electronic services. This increase in storage space has a negative impact on timely results 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. Moreover, Internet of Things nodes and CCTV cameras create a large source of affiliated crime data, containing possible traces and circumstantial evidence in addition to direct evidence. Thus, the Big Data paradigm is omnipresent in every aspect of crime investigation, creating obstacles in the work of agencies brought to maintain public order, safety, and security in society. Therefore, there is a strong need for advanced data analytics to aid crime investigations in cyber and physical worlds containing large-scale data, which requires novel approaches for more efficient and effective automated processing and reasoning.

List of research topics

  1. Novel intelligent methods and processing models for Big Data analytics
  • Machine learning-aided reasoning
  • Topic modeling and context analysis
  • Information security modeling and analytics
  • Intelligent decision support systems
  • Distributed computing and parallel optimization
  • Secure collaborative platforms for threat intelligence analysis and collection
  • Distributed storage and processing of data streams
  • Hardware and software architectures for large-scale data handling
  1. Crime-affiliated and simulated datasets
  • Release of new datasets
  • New data storage, taxonomy, and processing formats
  • Simulation of digital forensics data
  • Anonymized real case data
  1. Scenarios and use cases
  • Internet of Things and smart appliance forensics
  • Financial fraud detection and prevention
  • Cryptocurrencies and blockchain technologies in crime facilitation
  • Adversarial information gathering and social engineering
  • Smart Grid and energy under attack
  • Cyber-threat intelligence
  • Network forensics readiness
  • SCADA and industrial control systems (ICS)
  • Real-time video forensics and person-of-interest identification

Prof. Dr. Andrii Shalaginov
Dr. Ambika Shrestha Chitrakar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • advanced big data analytics
  • cybercrime detection and prevention
  • computer forensics
  • forensic intelligence
  • intelligent decision support systems
  • malware analysis
  • crimes in smart world
  • cyber situational awareness
  • intelligent cybersecurity
  • securing machine learning
  • knowledge discovery in attacks detection

Published Papers (1 paper)

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PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting
Future Internet 2020, 12(4), 66; https://doi.org/10.3390/fi12040066 - 12 Apr 2020
Cited by 2 | Viewed by 3340
Android malware has become the topmost threat for the ubiquitous and useful Android ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed. Too often, these solutions are either limited to research output or remain isolated [...] Read more.
Android malware has become the topmost threat for the ubiquitous and useful Android ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed. Too often, these solutions are either limited to research output or remain isolated and incapable of reaching end users or malware researchers. An earlier work named PACE (Platform for Android Malware Classification and Performance Evaluation), was introduced as a unified solution to offer open and easy implementation access to several machine-learning-based Android malware detection techniques, that makes most of the research reproducible in this domain. The benefits of PACE are offered through three interfaces: Representational State Transfer (REST) Application Programming Interface (API), Web Interface, and Android Debug Bridge (ADB) interface. These multiple interfaces enable users with different expertise such as IT administrators, security practitioners, malware researchers, etc. to use their offered services. In this paper, we propose PACER (Platform for Android Malware Classification, Performance Evaluation, and Threat Reporting), which extends PACE by adding threat intelligence and reporting functionality for the end-user device through the ADB interface. A prototype of the proposed platform is introduced, and our vision is that it will help malware analysts and end users to tackle challenges and reduce the amount of manual work. Full article
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