  Adversarial examples are known to mislead deep learning models to incorrectly
classify them, even in domains where such models achieve state-of-the-art
performance. Until recently, research on both attack and defense methods
focused on image recognition, primarily using convolutional neural networks
(CNNs). In recent years, adversarial example generation methods for recurrent
neural networks (RNNs) have been published, demonstrating that RNN classifiers
are also vulnerable to such attacks. In this paper, we present a novel defense
method, termed sequence squeezing, to make RNN classifiers more robust against
such attacks. Our method differs from previous defense methods which were
designed only for non-sequence based models. We also implement four additional
RNN defense methods inspired by recently published CNN defense methods. We
evaluate our methods against state-of-the-art attacks in the cyber security
domain where real adversaries (malware developers) exist, but our methods can
be applied against other discrete sequence based adversarial attacks, e.g., in
the NLP domain. Using our methods we were able to decrease the effectiveness of
such attack from 99.9% to 15%.
