Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Characterization and Detection of COVID-19 Themed Malicious
  Websites   COVID-19 has hit hard on the global community, and organizations are working
diligently to cope with the new norm of "work from home". However, the volume
of remote work is unprecedented and creates opportunities for cyber attackers
to penetrate home computers. Attackers have been leveraging websites with
COVID-19 related names, dubbed COVID-19 themed malicious websites. These
websites mostly contain false information, fake forms, fraudulent payments,
scams, or malicious payloads to steal sensitive information or infect victims'
computers. In this paper, we present a data-driven study on characterizing and
detecting COVID-19 themed malicious websites. Our characterization study shows
that attackers are agile and are deceptively crafty in designing geolocation
targeted websites, often leveraging popular domain registrars and top-level
domains. Our detection study shows that the Random Forest classifier can detect
COVID-19 themed malicious websites based on the lexical and WHOIS features
defined in this paper, achieving a 98% accuracy and 2.7% false-positive rate.

19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph