  One of deep learning's attractive benefits is the ability to automatically
extract relevant features for a target problem from largely raw data, instead
of utilizing human engineered and error prone handcrafted features. While deep
learning has shown success in fields such as image classification and natural
language processing, its application for feature extraction on raw network
packet data for intrusion detection is largely unexplored. In this paper we
modify a Word2Vec approach, used for text processing, and apply it to packet
data for automatic feature extraction. We call this approach Packet2Vec. For
the classification task of benign versus malicious traffic on a 2009 DARPA
network data set, we obtain an area under the curve (AUC) of the receiver
operating characteristic (ROC) between 0.988-0.996 and an AUC of the
Precision/Recall curve between 0.604-0.667.
