Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data   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.

37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin