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Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection

1
School of Computing Edinburgh Napier University, Edinburgh EH11 4DY, UK
2
Eight Bells LTD, Nicosia 2002, Cyprus
3
Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
*
Authors to whom correspondence should be addressed.
Technologies 2020, 8(4), 64; https://doi.org/10.3390/technologies8040064
Received: 9 October 2020 / Revised: 28 October 2020 / Accepted: 2 November 2020 / Published: 6 November 2020
(This article belongs to the Section Information and Communication Technologies)
Social media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual creation of fake users. Their goal is to deceive other users employing various methods, or even create a stream of fake news and opinions in order to influence an idea upon a specific subject, thus impairing the platform’s integrity. As such, machine learning techniques have been widely used in social networks to address this type of threat by automatically identifying fake accounts. Nonetheless, threat actors update their arsenal and launch a range of sophisticated attacks to undermine this detection procedure, either during the training or test phase, rendering machine learning algorithms vulnerable to adversarial attacks. Our work examines the propagation of adversarial attacks in machine learning based detection for fake Twitter accounts, which is based on AdaBoost. Moreover, we propose and evaluate the use of k-NN as a countermeasure to remedy the effects of the adversarial attacks that we have implemented. View Full-Text
Keywords: adversarial attacks; poisoning; social media; machine learning; Twitter adversarial attacks; poisoning; social media; machine learning; Twitter
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MDPI and ACS Style

Kantartopoulos, P.; Pitropakis, N.; Mylonas, A.; Kylilis, N. Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection. Technologies 2020, 8, 64. https://doi.org/10.3390/technologies8040064

AMA Style

Kantartopoulos P, Pitropakis N, Mylonas A, Kylilis N. Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection. Technologies. 2020; 8(4):64. https://doi.org/10.3390/technologies8040064

Chicago/Turabian Style

Kantartopoulos, Panagiotis, Nikolaos Pitropakis, Alexios Mylonas, and Nicolas Kylilis. 2020. "Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection" Technologies 8, no. 4: 64. https://doi.org/10.3390/technologies8040064

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