Abstract: We propose an adversarial attack for machine-learning-based network intrusion detection systems that selectively alters only the most influential features. Unlike conventional attacks such ...

Abstract: Machine learning plays a crucial role in autonomous vehicles, particularly in driver assistance technologies that enhance driving efficiency or eliminate the need for human intervention. One ...

ABSTRACT: This paper proposes a structured data prediction method based on Large Language Models with In-Context Learning (LLM-ICL). The method designs sample selection strategies to choose samples ...

ABSTRACT: To provide quantitative analysis of strategic confrontation game such as cross-border trades like tariff disputes and competitive scenarios like auction bidding, we propose an alternating ...

Two-party adversarial interactions are of fundamental importance in the study of cryptography and machine learning, as they allow us to work in dynamic environments to model and adapt to various ...

Corresponding repo for "Busting the Ballot: Voting Meets Adversarial Machine Learning". We show the security risk associated with using machine learning classifiers in United States election ...

We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...

The National Institute of Standards and Technology has issued a document that identifies threats associated with adversarial machine learning. The Adversarial Machine Learning: A Taxonomy and ...