Applying User Entity Behavioural Analytics (for Financial Sector)

As Cyber risk management focuses beyond prevention to detection, the need to automate the detection of cyber attacks or cyber threats have gained popularity, and thus the adoption of UEBA. The use of algorithms, data analytics, artificial intelligence and machine learning and statistical models and the availability of reliable data sources from various internal and external systems are crucial for the successful adoption of this solution.

Participants after this training will achieve the following:

• Aware of how UEBA is applied in cybersecurity risk and program management
• Understand the technologies, tools and processes applicable
• Understand the fundamentals of data analytics and its relevance for UEBA
• Understand the fundamentals of machine learning and deep learning and its relevance for UEBA
• Have the knowledge on relating the use of K-means Clustering, Classification, Regressions and Component Analysis in UEBA
• Knowledgeable with data source integration and cleansing management process for UEBA
• Equip with implementation requirements for UEBA
• Knowledgeable in using data visualization for UEBA presentation
• Aware of the challenges and limitation of UEBA
• Understand the best practices of UEBA

SOC analysts or security professionals or anyone who have responsibilities in cyber risks management or SOC operations