no code implementations • 9 Mar 2023 • Tucker Stewart, Bin Yu, Anderson Nascimento, Juhua Hu
For network administration and maintenance, it is critical to anticipate when networks will receive peak volumes of traffic so that adequate resources can be allocated to service requests made to servers.
no code implementations • 13 Oct 2022 • Mayana Pereira, Sikha Pentyala, Anderson Nascimento, Rafael T. de Sousa Jr., Martine De Cock
Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education.
no code implementations • 23 May 2022 • Sikha Pentyala, Nicola Neophytou, Anderson Nascimento, Martine De Cock, Golnoosh Farnadi
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity.
no code implementations • 5 Feb 2022 • Sikha Pentyala, Davis Railsback, Ricardo Maia, Rafael Dowsley, David Melanson, Anderson Nascimento, Martine De Cock
We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the protection of each owner's data.
no code implementations • 12 Mar 2020 • Raaghavi Sivaguru, Jonathan Peck, Femi Olumofin, Anderson Nascimento, Martine De Cock
We found that the DGA classifiers that rely on both the domain name and side information have high performance and are more robust against adversaries.
no code implementations • NeurIPS 2019 • Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, Anderson Nascimento
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few.
no code implementations • 3 May 2019 • Jonathan Peck, Claire Nie, Raaghavi Sivaguru, Charles Grumer, Femi Olumofin, Bin Yu, Anderson Nascimento, Martine De Cock
In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM. MI (a deep learning approach).
no code implementations • ICLR 2018 • Bin Yu, Jie Pan, Jiaming Hu, Anderson Nascimento, Martine De Cock
Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification.