Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Intrusion detection systems using classical machine learning techniques
  versus integrated unsupervised feature learning and deep neural network   Security analysts and administrators face a lot of challenges to detect and
prevent network intrusions in their organizations, and to prevent network
breaches, detecting the breach on time is crucial. Challenges arise while
detecting unforeseen attacks. This work includes a performance comparison of
classical machine learning approaches that require vast feature engineering,
versus integrated unsupervised feature learning and deep neural networks on the
NSL-KDD dataset. Various trials of experiments were run to identify suitable
hyper-parameters and network configurations of machine learning models. The DNN
using 15 features extracted using Principal Component analysis was the most
effective modeling method. The further analysis using the Software Defined
Networking features also presented a good accuracy using Deep Neural network.

29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex