Preprint Article Version 1 This version is not peer-reviewed

A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

Version 1 : Received: 10 May 2019 / Approved: 13 May 2019 / Online: 13 May 2019 (01:49:41 CEST)

A peer-reviewed article of this Preprint also exists.

Imam, N.H.; Vassilakis, V.G. A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment. Robotics 2019, 8, 50. Imam, N.H.; Vassilakis, V.G. A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment. Robotics 2019, 8, 50.

Journal reference: Robotics 2019, 8, 50
DOI: 10.3390/robotics8030050

Abstract

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine-learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors prone to a range of adversarial attacks. This paper thus surveys the attacks against Twitter spam detectors in an adversarial environment. In addition, a general taxonomy of potential adversarial attacks is proposed by applying common frameworks from the literature. Examples of adversarial activities on Twitter were provided after observing Arabic trending hashtags. A new type of spam tweet (Adversarial spam tweet), which can be used to undermine deployed classifier, were found. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors against such attacks are investigated.

Subject Areas

twitter spam detection; adversarial machine learning; online social networks; survey

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