Destructive Cyber Operations and Machine Learning
2020
Citations Over Time
Abstract
Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.
Related Papers
- → Dynamic Watermarking: Active Defense of Networked Cyber–Physical Systems(2016)264 cited
- → Analyzing Cyber-Physical Attacks on Networked Industrial Control Systems(2011)34 cited
- → Cyber-Physical Attack-Oriented Industrial Control Systems (ICS) Modeling, Analysis and Experiment Environment(2015)15 cited
- → Cyber Security Threats and Incidents in Industrial Control Systems(2020)4 cited
- Cyber-physical System Security for Networked Industrial Processes(2015)