Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection

Kantartopoulos, Panagiotis; Pitropakis, Nikolaos; Mylonas, Alexios; Kylilis, Nicolas

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.

Citation

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

• Journal Article Type: Article
• Acceptance Date: Nov 2, 2020
• Online Publication Date: Nov 6, 2020
• Publication Date: 2020-12
• Deposit Date: Nov 7, 2020
• Publicly Available Date: Nov 9, 2020
• Journal: Technologies
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 4
• Article Number: 64
• DOI: https://doi.org/10.3390/technologies8040064
• Keywords: adversarial attacks; poisoning; social media; machine learning; Twitter
• Public URL: http://researchrepository.napier.ac.uk/Output/2699529