MLsploit is the first user-friendly, cloud-based system that enables researchers and practitioners to rapidly evaluate and compare state-of-the-art adversarial attacks and defenses for machine learning (ML) models.
As recent advances in adversarial ML have revealed that many ML techniques are highly vulnerable to adversarial attacks, MLsploit meets the urgent need for practical tools that facilitate interactive security testing of ML models. MLsploit is jointly developed by researchers at Georgia Tech and Intel. Designed for extensibility, MLsploit accelerates the study and development of secure ML systems for safety-critical applications. MLsploit allows performing fast-paced experimentation with adversarial ML research that spans a diverse set of modalities, such as bypassing Android and Linux malware, or attacking and defending deep learning models for image classification.
Barnum: Detecting Document Malware via Control Flow Anomalies in Hardware Traces C. Yagemann, S. Sultana, L. Chen, W. Lee. 22nd Information Security Conference 2019, New York City, USA.
MLsploit: A Cloud-Based Framework for Adversarial Machine Learning Research N. Das, S. Li, C. Jeon, J. Jung*, S. T. Chen*, C. Yagemann*, E. Downing*, H. Park, E. Yang, L. Chen, M. E. Kounavis, R. Sahita, D. Durham, S. Buck, D. H. Chau, T. Kim, W. Lee. Black Hat Asia - Arsenal 2019, Singapore.
MLsploit: A Framework for Interactive Experimentation with Adversarial Machine Learning Research N. Das, S. Li, C. Jeon, J. Jung*, S. T. Chen*, C. Yagemann*, E. Downing*, H. Park, E. Yang, L. Chen, M. E. Kounavis, R. Sahita, D. Durham, S. Buck, D. H. Chau, T. Kim, W. Lee. KDD Workshop - Project Showcase 2019, Anchorage, AK, USA.
The Efficacy of SHIELD under Different Threat Models C. Cornelius, N. Das, S. T. Chen, L. Chen, M. E. Kounavis, D. H. Chau. KDD Workshop - Learning and Mining for Cybersecurity (LEMINCS) 2019, Anchorage, AK, USA.
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis L. Chen, C. Yagemann, E. Downing. CVPR 2019, Long Beach, CA, USA.
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio N. Das, M. Shanbhogue, S. T. Chen, L. Chen, M. E. Kounavis, D. H. Chau. European Conference on Machine Learning & Principles & Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018, Dublin, Ireland.
Compression to the Rescue: Defending from Adversarial Attacks Across Modalities N. Das, M. Shanbhogue, S. T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, D. H. Chau. KDD Workshop - Project Showcase 2018, London, England.
SHIELD: Fast, Practical Defense and Vaccination for Deep Learning Using JPEG Compression N. Das, M. Shanbhogue, S. T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, D. H. Chau. ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 2018, London, England.
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector S.-T. Chen, C. Cornelius, J. Martin, D. H. Chau. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) 2018, Dublin, Ireland.
AVPASS: Leaking and Bypassing Antivirus Detection Model Automatically J. Jung, C. Jeon, I. Yun, M. Wolotsky, T. Kim. Black Hat 2017, Las Vegas, CA, USA.
