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

no code implementations • 8 Jan 2021 • Olakunle Ibitoye, M. Omair Shafiq, Ashraf Matrawy

The need for robust, secure and private machine learning is an important goal for realizing the full potential of the Internet of Things (IoT).

Adversarial Robustness BIG-bench Machine Learning +1

A GAN-based Approach for Mitigating Inference Attacks in Smart Home Environment

no code implementations • 13 Nov 2020 • Olakunle Ibitoye, Ashraf Matrawy, M. Omair Shafiq

Beyond the notable risk of eavesdropping, intruders can adopt machine learning techniques to infer sensitive information from audio recordings on these devices, resulting in a new dimension of privacy concerns and attack variables to smart home users.

BIG-bench Machine Learning Generative Adversarial Network

The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

no code implementations • 6 Nov 2019 • Olakunle Ibitoye, Rana Abou-Khamis, Mohamed el Shehaby, Ashraf Matrawy, M. Omair Shafiq

We then introduce a classification of machine learning in network security applications.

BIG-bench Machine Learning Classification +3

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

no code implementations • 13 May 2019 • Olakunle Ibitoye, Omair Shafiq, Ashraf Matrawy

In this paper, we consider a variant of the FNN known as the Self-normalizing Neural Network (SNN) and compare its performance with the FNN for classifying intrusion attacks in an IoT network.

Adversarial Robustness Intrusion Detection