Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection for Scenario-based Insider Activities using CGAN
  Augmented Data   Insider threats are the cyber attacks from within the trusted entities of an
organization. Lack of real-world data and issue of data imbalance leave insider
threat analysis an understudied research area. To mitigate the effect of skewed
class distribution and prove the potential of multinomial classification
algorithms for insider threat detection, we propose an approach that combines
generative model with supervised learning to perform multi-class classification
using deep learning. The generative adversarial network (GAN) based insider
detection model introduces Conditional Generative Adversarial Network (CGAN) to
enrich minority class samples to provide data for multi-class anomaly
detection. The comprehensive experiments performed on the benchmark dataset
demonstrates the effectiveness of introducing GAN derived synthetic data and
the capability of multi-class anomaly detection in insider activity analysis.
Moreover, the method is compared with other existing methods against different
parameters and performance metrics.

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The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph