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        Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection

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        technologies_08_00064.pdf (PDF, 282Kb)
        Author
        Kantartopoulos, Panagiotis
        Pitropakis, Nikolaos
        Mylonas, Alexios
        Kylilis, Nicolas
        Attention
        2299/23875
        Abstract
        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.
        Publication date
        2020-11-06
        Published in
        Technologies
        Published version
        https://doi.org/10.3390/technologies8040064
        Other links
        http://hdl.handle.net/2299/23875
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