Data Science for Cyber Security

Dear Colleagues,

This issue is dedicated to the 1st International Workshop on Data Science for Cyber Security (DS4CS 2021), devoted to highlight the recent trends and progress made at the intersection of data science and cyber-security. Due to the COVID pandemic DS4CS 2021 will take place virtually, as is the case for the hosting conference on Cyber-Security and Resilience (IEEE-CSR 2021).

Over the years cyber-threats have increased in numbers and sophistication; adversaries now use a vast set of tools and tactics to attack their victims with their motivations ranging from intelligence collection to destruction or financial gain. Lately, the introduction of IoT devices on a number of applications, ranging from home automation to monitoring of critical infrastructures, has created an even more complicated cyber-defense landscape. The sheer number of IoT devices deployed globally, most of which are readily accessible and easily hacked, allows threat actors to use them as the cyber-weapon delivery system of choice in many today’s cyber-attacks, ranging from botnet-building for DDoS attacks, to malware spreading and spamming.

Staying on top of these evolving cyber-threats has become an increasingly difficult task that nowadays entails the collection, analysis, and leveraging of huge volumes of data and requires methodologies and techniques located at the intersection of statistics, data mining, machine learning, visualization and big data. Although the application of Data Science methodology to the Cyber Security domain is a relative new topic, it steadily gathers the interest of the research community as showcased by the utilization of data science techniques in a variety of cyber-defense facets that include proactive technologies (e.g., cyber-threat intelligence gathering and sharing), platform profiling (e.g., trust calculation and blacklisting), attack detection/mitigation (e.g., active network monitoring, situational awareness, and adaptable mitigation strategies), and others. This workshop aims to spotlight cutting-edge research in data science driven cyber-security in academia, business and government, as well as help in the alignment of these endeavors.

• Big data-driven cyber-security (incl. analytics, management)
• Machine and deep learning methods for cyber-security (incl. malware/phishing/botnet/ spam/intrusion/anomaly detection)
• Visualization methods (incl. visual situation awareness, VR & AR visualization, real-time visualization)
• AI-driven cybersecurity
• Private information retrieval
• Cyber-threat intelligence collection, identification and sharing at scale
• Private/sensitive information protection
• Machine-learning powered traffic analysis and attack modelling
• Machine learning-based platform profiling and trust management
• Advanced attack detection and mitigation

Prof. Dr. Christos Tryfonopoulos
Prof. Dr. Skiadopoulos Spiros
Guest Editors

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• data science
• cyber-security
• big data
• machine learning
• artificial intelligence