  Fuzzing is one of the most effective technique to identify potential software
vulnerabilities. Most of the fuzzers aim to improve the code coverage, and
there is lack of directedness (e.g., fuzz the specified path in a software). In
this paper, we proposed a deep learning (DL) guided directed fuzzing for
software vulnerability detection, named DeFuzz. DeFuzz includes two main
schemes: (1) we employ a pre-trained DL prediction model to identify the
potentially vulnerable functions and the locations (i.e., vulnerable
addresses). Precisely, we employ Bidirectional-LSTM (BiLSTM) to identify
attention words, and the vulnerabilities are associated with these attention
words in functions. (2) then we employ directly fuzzing to fuzz the potential
vulnerabilities by generating inputs that tend to arrive the predicted
locations. To evaluate the effectiveness and practical of the proposed DeFuzz
technique, we have conducted experiments on real-world data sets. Experimental
results show that our DeFuzz can discover coverage more and faster than AFL.
Moreover, DeFuzz exposes 43 more bugs than AFL on real-world applications.
