A multi-dimensional spam filtering framework based on threat intelligence
Xiaopeng Tian, Di Tang · 2019
Email remains the primary method used to initiate an advanced attack or deliver ransomware because it can be highly targeted and customized to increase the odds of exploitation. Most anti-spam technologies mitigate against traditional spam and viruses, but lack the automated analysis needed to catch spam campaigns and more dangerous threats from the first time they're seen. This paper proposes a new multidimensional spam filtering framework based on threat intelligence. The framework collaborates and classifies multiple mail filtering technologies, using threat intelligence mail data as a training set, combines static and dynamic analysis, threat intelligence analysis, and behavioral intention analysis to establish a multi-dimensional decision tree to identify and trace high-level email attacks.