6 papers
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes
Yael Mathov, Lior Rokach, Yuval Elovici
Adversarial examples have proven to be a concerning threat to deep learning models, particularly in the image domain. However, while many studies have examined adversarial examples…
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
Yael Mathov, Tal Ben Senior, Asaf Shabtai +1
Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy. These automated systems use learning models to analyze conversations, and calls that…
Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
Elior Nehemya, Yael Mathov, Asaf Shabtai +1
In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market'…
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
Yael Mathov, Eden Levy, Ziv Katzir +2
Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these do…
Challenges for Security Assessment of Enterprises in the IoT Era
Yael Mathov, Noga Agmon, Asaf Shabtai +3
For years, attack graphs have been an important tool for security assessment of enterprise networks, but IoT devices, a new player in the IT world, might threat the reliability of…
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
Yair Meidan, Michael Bohadana, Yael Mathov +4
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks. In order to mitig…