RBC Cyber Security Analytics - ON-077
Preferred Disciplines: Mathematics, Stats, Computer Science, Signal Processing, or Electrical Engineering (Masters or PhD)
Project Length: 8 months
Desired start date: January/February 2017
Location: Toronto, ON
No. of Positions: 4
Preferences: UofT, McMaster, Waterloo, Queen’s, UNB, UBC, UVic, Western
About the Company:
Royal Bank of Canada is Canada’s largest bank, and one of the largest banks in the world, based on market capitalization. We are one of North America’s leading diversified financial services companies, and provide personal and commercial banking, wealth management, insurance, investor services and capital markets products and services on a global basis. We employ approximately 78,000 full- and part-time employees who serve more than 16 million personal, business, public sector and institutional clients through offices in Canada, the U.S. and 39 other countries.
Study of the threat landscape reveals that the number of cyber-attacks has increased significantly over the past decade. Attackers are more sophisticated and better organized than any time before and the attacks are becoming more and more complex in nature. Traditional solutions, mostly based on signatures and predetermined rules, are therefore incapable of handling complex scenarios. Moreover, they lack required scalability and do not provide proper context and sufficient transparency into events and activities, which are essential for carrying out effective forensic activities.
RBC’s Cyber Analytics program leverages big data technologies to perform data analysis, pattern recognition, and predictive modelling using: i) the state-of-the-art machine learning techniques, and ii) approaches rooted in statistical learning theory.
The goal of the program is to provide the means for Proactive threat analysis mainly aimed at: i) identifying threats that have not been seen before, ii) providing the ability to anticipate cyber-attacks and drive proactive mitigation, detection and response, and iii) monitoring the environment for anomalous behavior (“normal” vs. “outside norm”) to identify malicious actors and potentially malicious patterns of behavior, hence fortifying the cybersecurity posture of RBC
- Probabilistic Graphical Models
- Statistical Learning Theory
- Manifold Learning
- Time Series Analysis
- Novelty/Outlier Detection
Expertise and Skills Needed:
- Knowledge of Machine Learning, theory and application
- Good understanding of Probability Theory and Statistics
- Programming experience with Python or Scala
For more info or to apply to this applied research position, please