Information Retrieval
Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Behavior-based modeling and its application to Email analysis
ACM Transactions on Internet Technology (TOIT)
Securing web service by automatic robot detection
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
Identifying web spam with user behavior analysis
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Unsupervised Spam Detection by Document Complexity Estimation
DS '08 Proceedings of the 11th International Conference on Discovery Science
Web Spam Identification with User Browsing Graph
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
HoneySpam 2.0: Profiling Web Spambot Behaviour
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part II
Proceedings of the CUBE International Information Technology Conference
How much money do spammers make from your website?
Proceedings of the CUBE International Information Technology Conference
The changing nature of Spam 2.0
Proceedings of the CUBE International Information Technology Conference
Improving network security and design using honeypots
Proceedings of the CUBE International Information Technology Conference
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Web spam is an escalating problem that wastes valuable resources, misleads people and can manipulate search engines in achieving undeserved search rankings to promote spam content. Spammers have extensively used Web robots to distribute spam content within Web 2.0 platforms. We referred to these web robots as spambots that are capable of performing human tasks such as registering user accounts as well as browsing and posting content. Conventional content-based and link-based techniques are not effective in detecting and preventing web spambots as their focus is on spam content identification rather than spambot detection. We extend our previous research by proposing two action-based features sets known as action time and action frequency for spambot detection. We evaluate our new framework against a real dataset containing spambots and human users and achieve an average classification accuracy of 94.70%.