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Bobby Filar
Publications - 13
Citations - 832
Bobby Filar is an academic researcher. The author has contributed to research in topics: Malware & Deep learning. The author has an hindex of 4, co-authored 13 publications receiving 625 citations.
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The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Miles Brundage,Shahar Avin,Jack Clark,Helen Toner,Peter Eckersley,Ben Garfinkel,Allan Dafoe,Paul Scharre,Thomas Zeitzoff,Bobby Filar,Hyrum S. Anderson,Heather M. Roff,Gregory C. Allen,Jacob Steinhardt,Carrick Flynn,Seán Ó hÉigeartaigh,Simon Beard,Haydn Belfield,Sebastian Farquhar,Clare Lyle,Rebecca Crootof,Owain Evans,Michael Page,Joanna J. Bryson,Roman V. Yampolskiy,Dario Amodei +25 more
TL;DR: The following organisations are named on the report: Future of Humanity Institute, University of Oxford, Centre for the Study of Existential Risk, Universityof Cambridge, Center for a New American Security, Electronic Frontier Foundation, OpenAI.
Proceedings ArticleDOI
DeepDGA: Adversarially-Tuned Domain Generation and Detection
TL;DR: The hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs is tested.
Posted Content
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning
TL;DR: This work proposes a more general framework based on reinforcement learning (RL) for attacking static portable executable (PE) anti-malware engines and shows in experiments that this method can attack a gradient-boosted machine learning model with evasion rates that are substantial and appear to be strongly dependent on the dataset.
Posted Content
DeepDGA: Adversarially-Tuned Domain Generation and Detection
TL;DR: In this article, a character-based generative adversarial network (GAN) was proposed to detect DGA variants on a per-domain basis, which provides a simple and flexible means to detect known DGA families.
Posted Content
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection
TL;DR: This work develops a new approach to temporal max pooling that makes the required memory invariant to the sequence length $T$, which makes MalConv more memory efficient, and up to $25.8\times$ faster to train on its original dataset, while removing the input length restrictions to Malconv.