Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Machine Learning Techniques for Windows Ransomware Network
  Traffic Detection   Ransomware has become a significant global threat with the
ransomware-as-a-service model enabling easy availability and deployment, and
the potential for high revenues creating a viable criminal business model.
Individuals, private companies or public service providers e.g. healthcare or
utilities companies can all become victims of ransomware attacks and
consequently suffer severe disruption and financial loss. Although machine
learning algorithms are already being used to detect ransomware, variants are
being developed to specifically evade detection when using dynamic machine
learning techniques. In this paper, we introduce NetConverse, a machine
learning analysis of Windows ransomware network traffic to achieve a high,
consistent detection rate. Using a dataset created from conversation-based
network traffic features we achieved a true positive detection rate of 97.1%
using the Decision Tree (J48) classifier.

21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom