Learning from Mutants - Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Offered By: IEEE via YouTube
Course Description
Overview
Explore a novel approach for constructing models of cyber-physical systems (CPS) automatically in this conference talk presented at the 2018 IEEE Symposium on Security & Privacy. Discover how supervised machine learning can be applied to data traces obtained after systematically seeding software components with faults ("mutants"). Learn about the efficacy of this method demonstrated on a simulator of a real-world water purification plant, including a framework for automatic mutant generation, data trace collection, and SVM-based model learning. Examine the use of cross-validation and statistical model checking to show how the learnt model characterizes an invariant physical property of the system. Understand the practical application of this invariant in detecting 85% of 55 network and code-modification attacks from runtime data logs, highlighting its potential for enhancing CPS security and monitoring.
Syllabus
Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a CPS
Taught by
IEEE Symposium on Security and Privacy
Tags
Related Courses
How to Win a Data Science Competition: Learn from Top KagglersHigher School of Economics via Coursera Data Science: Machine Learning
Harvard University via edX Visual Machine Learning with Yellowbrick
Coursera Project Network via Coursera Regression Analysis with Yellowbrick
Coursera Project Network via Coursera Support Vector Machines in Python, From Start to Finish
Coursera Project Network via Coursera