Forensics Data Analytics Enhancement via Artificial Intelligences and Big Data Analytics

Dear Colleagues,

With the rapid development and integration of new technologies, crimes in today’s connected world are committed not only in the physical realm but, equally or more commonly, in cyberspace. 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 were considered the main source of data processed in digital investigations. However, the changing nature of our world has brought forth previously unrecognized concepts like 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 the previously unforeseen consequences of attacks to life. The steady increase in digital storage size over the last decades has resulted in terabytes of data used in a single household, not to mention large electronic services. This increase in storage space had a negative impact on timely results for digital investigations as it can take up to several hours simply to image the hard drive, not to mention 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. Therefore, the Big Data paradigm is omnipresent in every aspect of crime investigations, creating obstacles for agencies expected to maintain public order, safety, and security. 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 modelling 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 appliances forensics;

- Financial fraud detection and prevention;

- Police investigations via AI-aided methodologies;

- Cryptocurrencies and blockchain technologies in crime facilitation;

- Adversarial information gathering and social engineering;

- Smart grid and energy under attacks;

- Cyber threats intelligence;

- Network forensics readiness;

- SCADA and Industrial Control Systems (ICS);

- Real-time video forensics and person-of-interest identification.

Dr. Andrii Shalaginov
Dr. Asif Iqbal
Prof. Dr. Mamoun Alazab
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
• secure machine learning
• knowledge discovery in attacks detection.