10 citations · 13 across the 6 of their papers we have counts for
6 papers
Segmentation Fault: A Cheap Defense Against Adversarial Machine Learning
Doha Al Bared, Mohamed Nassar
Recently published attacks against deep neural networks (DNNs) have stressed the importance of methodologies and tools to assess the security risks of using this technology in crit…
Hack The Box: Fooling Deep Learning Abstraction-Based Monitors
Sara Hajj Ibrahim, Mohamed Nassar
Deep learning is a type of machine learning that adapts a deep hierarchy of concepts. Deep learning classifiers link the most basic version of concepts at the input layer to the mo…
A Practical Scheme for Two-Party Private Linear Least Squares
Mohamed Nassar
Privacy-preserving machine learning is learning from sensitive datasets that are typically distributed across multiple data owners. Private machine learning is a remarkable challen…
On the Evaluation of the Privacy Breach in Disassociated Set-Valued Datasets
Sara Barakat, Bechara Al Bouna, Mohamed Nassar +1
Data anonymization is gaining much attention these days as it provides the fundamental requirements to safely outsource datasets containing identifying information. While some tech…
Outbound SPIT Filter with Optimal Performance Guarantees
Tobias Jung, Sylvain Martin, Mohamed Nassar +2
This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing,…
Intrusion detection mechanisms for VoIP applications
Mohamed El Baker Nassar, Radu State, Olivier Festor
VoIP applications are emerging today as an important component in business and communication industry. In this paper, we address the intrusion detection and prevention in VoIP netw…