FriSM: Malicious exploit kit detection via feature-based string-similarity matching

Abstract

Since an exploit kit (EK) was first developed, an increasing number of attempts has been made to infect users’ PCs by transmitting malware via EKs. To tackle such malware distribution, we propose herein an enhanced similarity-matching technique that determines whether the test sets are similar to the pattern sets in which the structural properties of EKs are defined. A key characteristic of our similarity-matching technique is that, unlike typical pattern-matching, it can detect isomorphic variants derived from EKs. In an experiment involving 36,950 datasets, our similarity-matching technique provides a TP rate of 99.9% and an FP rate of 0.001% with a performance of 0.003 s/page.