Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Black-Box Attacks against RNN based Malware Detection Algorithms   Recent researches have shown that machine learning based malware detection
algorithms are very vulnerable under the attacks of adversarial examples. These
works mainly focused on the detection algorithms which use features with fixed
dimension, while some researchers have begun to use recurrent neural networks
(RNN) to detect malware based on sequential API features. This paper proposes a
novel algorithm to generate sequential adversarial examples, which are used to
attack a RNN based malware detection system. It is usually hard for malicious
attackers to know the exact structures and weights of the victim RNN. A
substitute RNN is trained to approximate the victim RNN. Then we propose a
generative RNN to output sequential adversarial examples from the original
sequential malware inputs. Experimental results showed that RNN based malware
detection algorithms fail to detect most of the generated malicious adversarial
examples, which means the proposed model is able to effectively bypass the
detection algorithms.

19
The categories of this paper are: eess
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol