13 citations · 13 across the 2 of their papers we have counts for
7 papers
Examining Machine Learning for 5G and Beyond through an Adversarial Lens
Muhammad Usama, Rupendra Nath Mitra, Inaam Ilahi +2
Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allo…
Adversarial Machine Learning Attack on Modulation Classification
Muhammad Usama, Muhammad Asim, Junaid Qadir +2
Modulation classification is an important component of cognitive self-driving networks. Recently many ML-based modulation classification methods have been proposed. We have evaluat…
Adversarial ML Attack on Self Organizing Cellular Networks
Salah-ud-din Farooq, Muhammad Usama, Junaid Qadir +1
Deep Neural Networks (DNN) have been widely adopted in self-organizing networks (SON) for automating different networking tasks. Recently, it has been shown that DNN lack robustnes…
Black-box Adversarial ML Attack on Modulation Classification
Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha
Recently, many deep neural networks (DNN) based modulation classification schemes have been proposed in the literature. We have evaluated the robustness of two famous such modulati…
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?
Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha +1
The holy grail of networking is to create \textit{cognitive networks} that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the pr…
Caveat emptor: the risks of using big data for human development
Siddique Latif, Adnan Qayyum, Muhammad Usama +3
Big data revolution promises to be instrumental in facilitating sustainable development in many sectors of life such as education, health, agriculture, and in combating humanitaria…