Search Results for author: Anderson Nascimento

Found 8 papers, 0 papers with code

Enhancing Peak Network Traffic Prediction via Time-Series Decomposition

no code implementations9 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.

Time Series Traffic Prediction

Secure Multiparty Computation for Synthetic Data Generation from Distributed Data

no code implementations13 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.

Synthetic Data Generation

PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning

no code implementations23 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.

Attribute Decision Making +3

Training Differentially Private Models with Secure Multiparty Computation

no code implementations5 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.

Privacy Preserving

Inline Detection of DGA Domains Using Side Information

no code implementations12 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.

Adversarial Attack

CharBot: A Simple and Effective Method for Evading DGA Classifiers

no code implementations3 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).

Adversarial Attack

Character Level Based Detection of DGA Domain Names

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.

General Classification text-classification +1

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