Ashraf Matrawy

Analyzing Adversarial Attacks Against Deep Learning for Intrusion\n Detection in IoT Networks

2019 · arXiv (Cornell University)

Adversarial attacks have been widely studied in the field of computer vision\nbut their impact on network security applications remains an area of open\nresearch. As IoT, 5G and AI continue to converge to realize the promise …

DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning

2021 · arXiv (Cornell University)

The need for robust, secure and private machine learning is an important goal for realizing the full potential of the Internet of Things (IoT). Federated learning has proven to help protect against privacy violations and …

Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones

2023 · arXiv (Cornell University)

Machine Learning (ML) has become pervasive, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy compared to traditional models in processing and classifying large volumes …