Advanced Big Data Analytics for Cyber and Cyber-Physical Crime Investigations

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

2. 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

3. 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
Mr. Asif Iqbal CISSP, CISA, CISM, CRISC
Dr. Ambika Shrestha Chitrakar
Guest Editors

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• 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