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Found 5 papers, 1 papers with code
Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems
We show that the use of ML in medical systems, particularly connected systems that involve interfacing the ML engine with multiple peripheral devices, has security risks that might cause life-threatening damage to a patient's health in case of adversarial interventions.
LogShield: A Transformer-based APT Detection System Leveraging Self-Attention
Lately, there have been studies where transformer-based language models are being used to detect various types of attacks from system logs.
ANUBIS: A Provenance Graph-Based Framework for Advanced Persistent Threat Detection
We present ANUBIS, a highly effective machine learning-based APT detection system.
Analyzing the Usefulness of the DARPA OpTC Dataset in Cyber Threat Detection Research
In this work, we analyze the usefulness of the recently introduced DARPA Operationally Transparent Cyber (OpTC) dataset in this regard.
Cryptography and Security
Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective
In this article, we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness from a security engineering perspective, focusing on the problems in system threat analysis, design and evaluation faced in developing practical machine learning applications, in terms of robustness and user trust.
