Yang, Harkreader and Gu: Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers
نویسندگان
چکیده
To date, as one of the most popular Online Social Networks (OSNs), Twitter is paying its dues as more and more spammers set their sights on this microblogging site. Twitter spammers can achieve their malicious goals such as sending spam, spreading malware, hosting botnet command and control (C&C) channels, and launching other underground illicit activities. Due to the significance and indispensability of detecting and suspending those spam accounts, many researchers along with the engineers in Twitter Inc. have devoted themselves to keeping Twitter as spam-free online communities. Most of the existing studies utilize machine learning techniques to detect Twitter spammers. “While the priest climbs a post, the devil climbs ten.” Twitter spammers are evolving to evade existing detection features. In this paper, we first make a comprehensive and empirical analysis of the evasion tactics utilized by Twitter spammers. We further design several new detection features to detect more Twitter spammers. In addition, to deeply understand the effectiveness and difficulties of using machine learning features to detect spammers, we analyze the robustness of 24 detection features that are commonly utilized in the literature as well as our proposed ones. Through our experiments, we show that our new designed features are much more effective to be used to detect (even evasive) Twitter spammers. According to our evaluation, while keeping an even lower false positive rate, the detection rate using our new feature set is also significantly higher than that of existing work. To the best of our knowledge, this work is the first empirical study and evaluation of the effect of evasion tactics utilized by Twitter spammers and is a valuable supplement to this line of research.
منابع مشابه
Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers
To date, as one of the most popular Online Social Networks (OSNs), Twitter is paying its dues as more and more spammers set their sights on this microblogging site. The Twitter spammers can achieve their malicious goals such as sending spam, spreading malware, hosting botnet command and control (C&C) channels, and launching other underground illicit activities. Due to the significance and indis...
متن کاملSpammers Are Becoming "Smarter" on Twitter
T witter has become one of the most commonly used communication tools in daily life. With 500 million users, Twitter now generates more than 500 million tweets per day. However, its popularity has also attracted spamming. Spammers spread many intensive tweets, which can lure legitimate users to commercial or malicious sites containing malware downloads, phishing, drug sales, scams, and more.1 S...
متن کاملDetecting Social Spam Campaigns on Twitter
The popularity of Twitter greatly depends on the quality and integrity of contents contributed by users. Unfortunately, Twitter has attracted spammers to post spam content which pollutes the community. Social spamming is more successful than traditional methods such as email spamming by using social relationship between users. Detecting spam is the first and very critical step in the battle of ...
متن کاملA Domain-Agnostic Approach to Spam-URL Detection via Redirects
Web services like social networks, video streaming sites, etc. draw numerous viewers daily. This popularity makes them attractive targets for spammers to distribute hyperlinks to malicious content. In this work we propose a new approach for detecting spam URLs on the Web. Our key idea is to leverage the properties of URL redirections widely deployed by spammers. We combine the redirect chains i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013