We here proudly list all of our research awards.
SBSEG Distinguished Paper
Marcus Botacin distinguished paper certification from @SBSEG2019.
By André Grégio and Marcus Botacin
Distinguished paper @SBSEG2019
Marcus Botacin’s paper has been awarded as a distinguished paper during 2019’s SBSEG. Check out the news here.
USENIX Enigma Diversity Grant
Marcus Botacin, Daniela Oliveira and Fabrício Ceschin at USENIX Enigma 2019.
By Fabrício Ceschin and Marcus Botacin
Support to attend USENIX Enigma 2019 at San Francisco, CA, USA
To encourage diversity in advanced computing, Enigma 2019 offered grants to support computer scientists interested in attending the conference. Fabrício Ceschin and Marcus Botacin were awarded and both researchers could attend the conference for the first time. Enigma’s diversity grants were sponsored by Dropbox, Shopify, Microsoft, Kaspersky lab, Uber and Stripe.
SBSeg Best Dissertation Award
Marcus Botacin presenting his work at SBSeg 2018.
By Marcus Botacin and André Grégio
Brazilian Computer Society (SBC) Award for the best Master dissertation in 2018
Marcus Botacin’s dissertation “Hardware-Assisted Malware Analysis”, advised by Paulo de Geus and co-advised by André Grégio was awarded the best security dissertation during 2018’s Brazilian Security Symposium (SBSeg).
(Latin America Research Awards)
Researchers awarded by Google LARA in 2017
By André Grégio and Fabrício Ceschin
Identifying Concept-Drift in Malware Classifiers and the Applicability of Deep-Learning Detectors for APTs
The intent of the Google Research Awards is to support cutting-edge research in Computer Science, Engineering, and related fields. We won this award with the initial proposal of our framework “Need for Speed”, written by André Grégio and Fabrício Ceschin, which resulted in a published paper in IEEE Security & Privacy Magazine. In this project, we proposed to develop novel, adaptive models to identify whether a program is benign or malicious during its execution, even when its behavior is subtle, creating a real-time infection detector. We also proposed to create a public dataset holding as many features from malicious programs as possible, which will benefit from our proposed techniques to be always up-to-date. The models will rely on deep learning for multi-stage classification, as well as innovative concept-drift detection techniques (WIP). More info about the Google LARA 2017 award here (in portuguese).