Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Malicious Flow Detection   Cyber security has grown up to be a hot issue in recent years. How to
identify potential malware becomes a challenging task. To tackle this
challenge, we adopt deep learning approaches and perform flow detection on real
data. However, real data often encounters an issue of imbalanced data
distribution which will lead to a gradient dilution issue. When training a
neural network, this problem will not only result in a bias toward the majority
class but show the inability to learn from the minority classes. In this paper,
we propose an end-to-end trainable Tree-Shaped Deep Neural Network (TSDNN)
which classifies the data in a layer-wise manner. To better learn from the
minority classes, we propose a Quantity Dependent Backpropagation (QDBP)
algorithm which incorporates the knowledge of the disparity between classes. We
evaluate our method on an imbalanced data set. Experimental result demonstrates
that our approach outperforms the state-of-the-art methods and justifies that
the proposed method is able to overcome the difficulty of imbalanced learning.
We also conduct a partial flow experiment which shows the feasibility of
real-time detection and a zero-shot learning experiment which justifies the
generalization capability of deep learning in cyber security.

20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an