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CSCY 115 Cybersecurity Data Analytics: Reading list

CSCY 115 Cybersecurity Data Analytics

Department: BSc Computer Science

Module Description: This module focuses on utilizing big data analytics to enhance cybersecurity threat intelligence. Students will gain knowledge and skills in real-time cyber-attack prediction and mitigation using Security Event & Incident Management (SEIM) platforms and big data analytics. They will learn techniques for detecting and analyzing threats such as malware, ransomware, and Advanced Persistent Threats (APTs). The module emphasizes the correlation and analysis of diverse data sources to identify relevant cyber security incidents. Students will also engage in active threat hunting and explore visualizations for effective security analytics. Through a hands-on approach, they will develop expertise in applying data analytics techniques to detect and respond to cybersecurity threats. Indicative topics for the module include: Introduction to SIEM platforms and big data analytics; Machine Learning; Active threat hunting and security visualization; Malware, ransomware and APT detection; and Stream data processing for real-time cyber threat analysis.

Module texts

  • Michael I. Kaplan, M. I. (2022). Certified threat intelligence analysis manager: course workbook and study guide. Phase2 Advantage.

  • Palacin, V. (2021). Practical threat intelligence and data-driven threat hunting. Packt Publishing.

  • Savas, O. and Deng, J. (2019). Big data analytics in cyber security. Taylor & Francis.

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