Shady paths: leveraging surfing crowds to detect malicious web pages

130 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Practical Evasion of a Learning-Based Classifier: A Case Study (2014, background): “Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier.”
Malicious URL Detection using Machine Learning: A Survey (2017, background): “[74] incorporated information on redirection chains into redirection”
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data (2014, background): “I NTRODUCTION The cybersecurity landscape is evolving constantly.”
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (2018, methodology): “Machine learning has become a standard tool for malware research in the academic security community: it has been used in a wide range of domains including Windows malware [9, 28, 41], PDF malware [22, 26], malicious URLs [23, 39], Javascript malicious code [8, 35], and Android malware [4, 10, 15, 27”
When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors (2016, background): “However, most classifier evasion attempts, including nine targeted mimicry scenarios from two recent studies, are given an outcome of uncertain indicating that these observations cannot be given a reliable prediction by the classifier.”