The base-rate fallacy and its implications for the difficulty of intrusion detection
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Identifying video spammers in online social networks
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Detecting spammers and content promoters in online video social networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Uncovering social spammers: social honeypots + machine learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
@spam: the underground on 140 characters or less
Proceedings of the 17th ACM conference on Computer and communications security
Detecting and characterizing social spam campaigns
Proceedings of the 17th ACM conference on Computer and communications security
Detecting spammers on social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Who is tweeting on Twitter: human, bot, or cyborg?
Proceedings of the 26th Annual Computer Security Applications Conference
Proceedings of the 21st international conference on World Wide Web
Poultry markets: on the underground economy of twitter followers
Proceedings of the 2012 ACM workshop on Workshop on online social networks
Efficient and scalable socware detection in online social networks
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Poultry markets: on the underground economy of twitter followers
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
Twitter games: how successful spammers pick targets
Proceedings of the 28th Annual Computer Security Applications Conference
Detecting malicious tweets in trending topics using a statistical analysis of language
Expert Systems with Applications: An International Journal
Searching for spam: detecting fraudulent accounts via web search
PAM'13 Proceedings of the 14th international conference on Passive and Active Measurement
An analysis of socware cascades in online social networks
Proceedings of the 22nd international conference on World Wide Web
Follow the green: growth and dynamics in twitter follower markets
Proceedings of the 2013 conference on Internet measurement conference
Exploring discriminatory features for automated malware classification
DIMVA'13 Proceedings of the 10th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
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Due to the significance and indispensability of detecting and suspending Twitter spammers, many researchers along with the engineers in Twitter Corporation have devoted themselves to keeping Twitter as spam-free online communities. Meanwhile, Twitter spammers are also evolving to evade existing detection techniques. In this paper, we make an empirical analysis of the evasion tactics utilized by Twitter spammers, and then design several new and robust features to detect Twitter spammers. Finally, we formalize 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 effective to detect Twitter spammers, achieving a much higher detection rate than three state-of-the-art approaches [35,32,34] while keeping an even lower false positive rate.