Prior works on detection of typosquatting abuse are based on the calculation of edit distance between domains. They do not fully utilize the context information of domains

and usually give many false positive results for short domains. Actively crawling much related information of the given domains can help improving the results

but introduce a heavy overhead. Therefore

we design a lightweight detecting strategy based on domain names

and introduce the bi-directional long short-term memory (LSTM) model to make full use of the domain context information. Furthermore

we give a locality sensitive hashing function for domain names

in order to increase the speed of typosquatting abuse detection over large-scale domain sets. Experimental results on a real data set show that the proposed method can overcome the shortcomings of edit distance based methods

and can detect typosquatting abuse efficiently.