Open AccessBook
Adversarial Machine Learning
Yevgeniy Vorobeychik,Murat Kantarcioglu +1 more
- 08 Aug 2018
169
TL;DR: The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed in education and research.
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Abstract: The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed ac...
read more
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Citations
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
TL;DR: In this article, the authors present an in-depth overview of the various challenges associated with the application of ML in vehicular networks and formulate the ML pipeline of CAVs and present various potential security issues associated with adoption of ML methods.
182
Deep Learning for Wireless Communications
Tugba Erpek,Timothy J. O'Shea,Yalin E. Sagduyu,Yi Shi,T. Charles Clancy +4 more
- 12 May 2020
TL;DR: In this paper, the authors used deep learning to design an end-to-end communication system using autoencoders and showed the benefits of deep learning in spectrum situation awareness ranging from channel modeling and estimation to signal detection and classification tasks.
143
Adversarial machine learning in Network Intrusion Detection Systems
TL;DR: This work explores the use of evolutionary computation and deep learning as tools for adversarial example generation and highlights the vulnerability of machine learning based NIDS in the face of adversarial perturbation.
141
Applicability of machine learning in spam and phishing email filtering: review and approaches
TL;DR: This paper elucidates on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set, and facilitates an exhaustive comparative study using several state-of-the-art machine learning algorithms.
139
IoT Network Security from the Perspective of Adversarial Deep Learning
Yalin E. Sagduyu,Yi Shi,Tugba Erpek +2 more
- 10 Jun 2019
TL;DR: This work presents new techniques built upon adversarial machine learning and applies them to three types of over-the-air (OTA) wireless attacks, namely denial of service (DoS) attack in terms of jamming, spectrum poisoning attack, and priority violation attack and introduces a defense mechanism that systematically increases the uncertainty of the adversary at the inference stage and improves the performance.
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