  Misspelled words of the malicious kind work by changing specific keywords and
are intended to thwart existing automated applications for cyber-environment
control such as harassing content detection on the Internet and email spam
detection. In this paper, we focus on malicious spelling correction, which
requires an approach that relies on the context and the surface forms of
targeted keywords. In the context of two applications--profanity detection and
email spam detection--we show that malicious misspellings seriously degrade
their performance. We then propose a context-sensitive approach for malicious
spelling correction using word embeddings and demonstrate its superior
performance compared to state-of-the-art spell checkers.
