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20182020
most citedPoisoning Attacks on Cyber Attack Detectors for Industrial Control Systems

32 citations · 33 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR202032 cited

Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems

Moshe Kravchik, Battista Biggio, Asaf Shabtai

Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detecto…

cs.CV2020

The Translucent Patch: A Physical and Universal Attack on Object Detectors

Alon Zolfi, Moshe Kravchik, Yuval Elovici +1

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order…

cs.LG20201 cited

Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks

Moshe Kravchik, Asaf Shabtai

In recent years, a variety of effective neural network-based methods for anomaly and cyber attack detection in industrial control systems (ICSs) have been demonstrated in the liter…

cs.CR2019

Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

Moshe Kravchik, Asaf Shabtai

Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems hav…

cs.CR2018

Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

Moshe Kravchik, Asaf Shabtai

This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The…