250 citations · 863 across the 66 of their papers we have counts for
6 papers · 1 filter
Make out like a (Multi-Armed) Bandit: Improving the Odds of Fuzzer Seed Scheduling with T-Scheduler
Simon Luo, Adrian Herrera, Paul Quirk +3
Fuzzing is a highly-scalable software testing technique that uncovers bugs in a target program by executing it with mutated inputs. Over the life of a fuzzing campaign, the fuzzer…
SplITS: Split Input-to-State Mapping for Effective Firmware Fuzzing
Guy Farrelly, Paul Quirk, Salil S. Kanhere +2
Ability to test firmware on embedded devices is critical to discovering vulnerabilities prior to their adversarial exploitation. State-of-the-art automated testing methods rehost f…
ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains
Fei Chen, Hoa Van Nguyen, David A. Taggart +3
Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio…
Ember-IO: Effective Firmware Fuzzing with Model-Free Memory Mapped IO
Guy Farrelly, Michael Chesser, Damith C. Ranasinghe
Exponential growth in embedded systems is driving the research imperative to develop fuzzers to automate firmware testing to uncover software bugs and security vulnerabilities. But…
Icicle: A Re-Designed Emulator for Grey-Box Firmware Fuzzing
Michael Chesser, Surya Nepal, Damith C. Ranasinghe
Emulation-based fuzzers enable testing binaries without source code, and facilitate testing embedded applications where automated execution on the target hardware architecture is d…
Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness
Bao Gia Doan, Shuiqiao Yang, Paul Montague +6
We present a new algorithm to train a robust malware detector. Modern malware detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations…