Threat is in the Air: Machine Learning for Wireless Network Applications

被引：8

作者：

Pajola, Luca ^{[1
]}

Pasa, Luca ^{[2
]}

Conti, Mauro ^{[1
]}

机构：

[1] Univ Padua, Padua, Italy

[2] Ist Italiano Tecnol, Ferrara, Italy

来源：

PROCEEDINGS OF THE 2019 ACM WORKSHOP ON WIRELESS SECURITY AND MACHINE LEARNING (WISEML '19) | 2019年

关键词：

Wireless network applications; machine learning; adversarial machine learning; security; SECURITY;

D O I：

10.1145/3324921.3328783

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

With the spread of wireless application, huge amount of data is generated every day. Thanks to its elasticity, machine learning is becoming a fundamental brick in this field, and many of applications are developed with the use of it and the several techniques that it offers. However, machine learning suffers on different problems and people that use it often are not aware of the possible threats. Often, an adversary tries to exploit these vulnerabilities in order to obtain benefits; because of this, adversarial machine learning is becoming wide studied in the scientific community. In this paper, we show state-of-the-art adversarial techniques and possible countermeasures, with the aim of warning people regarding sensible argument related to the machine learning.

引用

页码：16 / 21

页数：6