Student research highlight: Secure and resilient distributed machine learning under adversarial environments

Rui Zhang, Quanyan Zhu

Research output: Contribution to journalReview articlepeer-review

Abstract

Machine learning algorithms, such as support vector machines (SVMs), neutral networks, and decision trees (DTs) have been widely used in data processing for estimation and detection. They can be used to classify samples based on a model built from training data. However, under the assumption that training and testing samples come from the same natural distribution, an attacker who can generate or modify training data will lead to misclassification or misestimation. For example, a spam filter will fail to recognize input spam messages after training crafted data provided by attackers [1].

Original languageEnglish (US)
Article number7478408
Pages (from-to)34-36
Number of pages3
JournalIEEE Aerospace and Electronic Systems Magazine
Volume31
Issue number3
DOIs
StatePublished - Mar 2016

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Student research highlight: Secure and resilient distributed machine learning under adversarial environments'. Together they form a unique fingerprint.

Cite this