Artificial Neural Network and Deep Learning in Cybersecurity
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 August 2025 | Viewed by 58
Special Issue Editors
Interests: data privacy in big data; privacy-preserving data analytics; privacy-preserving social network data; improving privacy awareness in data sharing
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; data profiling; data integration and data warehousing; knowledge representation and management; data mining; big data; data science; intelligent systems; data streams; data privacy; digital health; human-computer interaction; data visualization; IoT data analytics; distributed and parallel computing; social network analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advances in information technology have led to numerous benefits and opportunities for industries, individuals, and society due to the increasing availability of data. The use of analytics approaches has led to the development of increasingly sophisticated applications, spanning from personalized medicine and e-commerce to crowd management and fraud detection. However, these applications have also introduced new cybersecurity challenges that need to be managed and monitored, significantly when such threats impact the real life of end-users. Cybersecurity represents an application context in which the demand for practical and effective solutions grows as fast as new threats develop.
This Special Issue will publish high-quality and original research articles in the context of cybersecurity by focusing on neural networks and deep learning-based methodologies/techniques for identifying and preventing cybersecurity issues. In particular, research topics will include methodologies and strategies addressing cybersecurity aspects affecting real society, such as the definition of anonymization strategies to be applied over sensitive data in a data analytics scenario, privatization strategies enabling data processing, privacy-preserving methodologies for social network data, strategies and methodologies to help users in being aware of privacy threats linked to the mismanagement of sensitive data and a definition of new proposals for cyber social security.
Dr. Domenico Desiato
Dr. Stefano Cirillo
Guest Editors
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Keywords
- data security
- data anonymization
- data privatization
- privacy-preserving social network data
- neural networks and deep learning privacy-preserving approaches
- security prevention
- data analysis
- privacy awareness in cybersecurity scenario
- cybersecurity
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