  Cyberattacks conducted by malicious hackers cause irreparable damage to
organizations, governments, and individuals every year. Hackers use exploits
found on hacker forums to carry out complex cyberattacks, making exploration of
these forums vital. We propose a hacker forum entity recognition framework
(HackER) to identify exploits and the entities that the exploits target. HackER
then uses a bidirectional long short-term memory model (BiLSTM) to create a
predictive model for what companies will be targeted by exploits. The results
of the algorithm will be evaluated using a manually labeled gold-standard test
dataset, using accuracy, precision, recall, and F1-score as metrics. We choose
to compare our model against state of the art classical machine learning and
deep learning benchmark models. Results show that our proposed HackER BiLSTM
model outperforms all classical machine learning and deep learning models in
F1-score (79.71%). These results are statistically significant at 0.05 or lower
for all benchmarks except LSTM. The results of preliminary work suggest our
model can help key cybersecurity stakeholders (e.g., analysts, researchers,
educators) identify what type of business an exploit is targeting.
