An Introduction to Adversarial Machine Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-11-25

AUTHORS

Atul Kumar , Sameep Mehta , Deepak Vijaykeerthy

ABSTRACT

Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (e.g., training data collection, training, operation). All model classes of machine learning systems can be misled by providing carefully crafted inputs making them wrongly classify inputs. Maliciously created input samples can affect the learning process of a ML system by either slowing the learning process, or affecting the performance of the learned model or causing the system make error only in attacker’s planned scenario. Because of these developments, understanding security of machine learning algorithms and systems is emerging as an important research area among computer security and machine learning researchers and practitioners. We present a survey of this emerging area named Adversarial machine learning. More... »

PAGES

293-299

Book

TITLE

Big Data Analytics

ISBN

978-3-319-72412-6
978-3-319-72413-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-72413-3_20

DOI

http://dx.doi.org/10.1007/978-3-319-72413-3_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092997377


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