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Found 5 papers, 0 papers with code
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors.
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes.
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
This finding suggests that neural network-based attack detectors used in the cyber-physical domain are more robust to poisoning than in other problem domains, such as malware detection and image processing.
Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA
Finally, we study the proposed method's robustness against adversarial attacks, that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect.
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks
This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks.
