***Introduction and Motivation***
Personal computers, laptops and personal smart devices have had a steady increase in storage and computational capacity capabilities over the years, where it has become common with terabytes in storage space. Moreover, the emergence of the Internet of Things and Smart Applications bringing a new horizon into how the data affects our life and the world around us. Despite the role of modern technologies in improved quality of life and making the world better place, the surface of cyber threats and anticipated cyber-attacks has been brought to a new level, as it is seen by ransomware and Mirai IoT botnets. Conventional Computer Forensics is no longer efficient because the term Computer has acquired much broader meaning over the last decades. Multiple aforementioned disruptive technologies result in the agile ICT environment, which constantly changes its state as a response to external influence.
Moreover, previously unseen things like IoT orphan devices become a standard practice due to the relative inexpensiveness of the technologies, connectivity and power supply. We are surrounded by interconnected components, which we might not know about, and those collect personal data, sensitive information and, often, has multiple security vulnerabilities. Big data paradigm is undeniable in every aspect of cybercrime investigations. As a result, there is a strong need for novel methods in future to aid cybercrime investigations and police on large-scale data. Therefore, it is essential to look for an advanced Artificial Intelligence method capable of handling such challenges and bringing down the amount of manual labour required by police officers. Finally, providing more efficient data handling and digital evidence discovery will build a strong foundation for intelligent decision support in future smart cities across the globe.

LINK: https://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/artificial-intelligence-for-cyber-defence-and-smart-policing

***Background***
It is clear that current State-of-the-Art tools used in Cyber Crime Investigation are based on trivial keyword search and manual content exploration by a human expert. At the same time, recent developments also added approximate pattern matching and so-called e-Discovery that helps and simplifies manual work of forensics investigations. However, Big Data problems need a different approach to tackle large-scale data seized or correlated for a criminal case. Therefore, there is a strong need to apply Artificial Intelligence (AI) and Automated Reasoning models capable of fast training and timely data processing.
On the other hand, response time requirements and accuracy limitations will require reaching a trade-off between the AI model interpretability, the time needed to train and accuracy concerning the desired solution. On the other hand, it is essential to implement also additional advanced data analytics models such as Hybrid Intelligent Systems or Soft Computing. This fact will bring together multiple AI models to build more accurate methods, or Recommender Systems, designed to stimulate the discovery of similar contextual situations and relevant evidential material. The future policing should also cover the detection of new attacks, the discovery of new adversarial trends and prediction of any future malicious patters from the available historical data. Finally, such smart data handling will result in building intelligent digital evidence processing systems that will help police officers to provide timely response to ongoing crimes and best possible assistance in the investigation of committed crimes.


***The Scope of the Special Issue***
The objective of the Special Issue AICDSP, journal Future Generation Computer Systems (FGCS), 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 through advertisements in relevant Information Security, Machine Learning and Digital Forensics-related public communication channels. Moreover, following the positive feedback and great interest last years, authors of nominated best papers will be invited to submit their extended contributions from the 4th International Workshop on Big Data Analytic for Cybercrime Investigation and Prevention 2020.


***Selection and Evaluation Criteria***
• Relevance to the cybercrime investigation and police investigations
• Applicability and technical soundness of Artificial Intelligence
• Research novelty and impact of the submitted work
• Readability and technical quality
• Availability of research implementation (Proof-of-concept)


***Research Topics***
- Social Network Analysis (SNA)
- Digital forensic intelligence (DFINT)
- Open-source Intelligence (OSINT)
- Internet of Things Forensics
- Forensic Data Analytics (FDA)
- Smart Appliances Forensics
- Drone Forensics
- Smart Grid Forensics
- Smart Car Forensics
- Cyber Threats Intelligence Sharing
- The ongoing integration of Artificial Intelligence methodologies in Law Enforcement
- Deep Learning models to combat cybercrime
- Decision Support of the Police Investigation
- Data Analytics for privacy-by-design in eHealth Sector
- Artificial Intelligence as a component in the NIST Cyber Security framework
- Analysis of cryptocurrency-enabled crimes
- Malware: Roles in cyber attacks, it impacts and underground economy of malware
- Covert Communication detection (Steganography, use Chat or other applications)
- Abusive, Fake and hate speech detection, prevention and origin tracking


***Important Dates***
Submission due: January 10, 2021
Notification: March 20, 2021
Camera-ready submission: April 30, 2021

It is important that authors select the name of the special issue (VSI:AICDSP) when they upload their manuscripts: https://www.evise.com/profile/api/navigate/FGCS


***Guest Editors***
• Andrii Shalaginov, andrii.shalaginov@ntnu.no, Norwegian University of Science and Technology, Gjøvik, Norway
• Mamoun Alazab, mamoun.alazab@cdu.edu.au, College of Engineering, IT and Environment at Charles Darwin University, Australia
• Ajit Kumar, ajit.kumar@vitbhopal.ac.in, VIT Bhopal, Bhopal, India
• Laurence T. Yang, ltyang@stfx.ca, Department of Computer Science, St. Francis Xavier University, Antigonish, Canada
• Houbing Song, songh4@erau.edu, Security and Optimization for Networked Globe Laboratory (SONG Lab), Embry-Riddle Aeronautical University, Daytona Beach, USA