32 citations · 53 across the 7 of their papers we have counts for
7 papers
Leveraging Code Cohesion Analysis to Identify Source Code Supply Chain Attacks
Maor Reuben, Ido Mendel, Or Feldman +3
Supply chain attacks significantly threaten software security with malicious code injections within legitimate projects. Such attacks are very rare but may have a devastating impac…
Rule-ATT&CK Mapper (RAM): Mapping SIEM Rules to TTPs Using LLMs
Prasanna N. Wudali, Moshe Kravchik, Ehud Malul +3
The growing frequency of cyberattacks has heightened the demand for accurate and efficient threat detection systems. SIEM platforms are important for analyzing log data and detecti…
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…
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…
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…
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…
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…