Search Results for author:
Found 7 papers, 2 papers with code
How Generalizable are Deepfake Detectors? An Empirical Study
Deepfake videos and images are becoming increasingly credible, posing a significant threat given their potential to facilitate fraud or bypass access control systems.
Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs).
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models.
Active Fuzzing for Testing and Securing Cyber-Physical Systems
Cyber-physical systems (CPSs) in critical infrastructure face a pervasive threat from attackers, motivating research into a variety of countermeasures for securing them.
Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage.
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS).
Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation
Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network.
