Machine Learning

Book · Fri Apr 21 04:00:00 EDT 2017

DOI:https://doi.org/10.1016/B978-0-12-805349-2.00006-6· OSTI ID:1378049

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1378049
• Report Number(s): PNNL-SA-122747; 400403109
• Country of Publication: United States
• Language: English

Related Subjects

Anomaly detection
Bayesian networks
cluster analysis
hidden Markov model
machine learning
regression