Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

DNA-inspired characterization and detection of novel social Twitter spambots

DNA-inspired characterization and detection of novel social Twitter spambots

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Nature-Inspired Cyber Security and Resiliency: Fundamentals, Techniques and Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Spambot detection is a must for the protection of cyberspace, in terms of both threats to sensitive information of users and trolls that may want to cheat and influence the public opinion. Unfortunately, new waves of malicious accounts are characterized by advanced features, making their detection extremely challenging. In contrast with the supervised spambot detectors largely used in recent years and inspired by biological DNA, we propose an alternative, unsupervised detection approach. Its novelty is based on the idea of modeling online user behaviors with strings of characters representing the sequence of the user's online actions. Exploiting this nature-inspired behavioral model, the proposed technique lets groups of spambots emerge from the crowd, by comparing the accounts' behaviors. Results show that the proposal outperforms the best-of-breed algorithms commonly employed for spambot detection.

Chapter Contents:

  • 10.1 Introduction
  • 10.1.1 Contributions
  • 10.2 Datasets
  • 10.2.1 Social spambots
  • 10.2.2 Legitimate accounts
  • 10.2.3 Reproducibility
  • 10.3 Classification task and classification metrics
  • 10.4 Benchmarking current spambot detection techniques
  • 10.4.1 The BotOrNot? service
  • 10.4.2 Supervised spambot classification
  • 10.4.3 Unsupervised spambot detection via Twitter stream clustering
  • 10.4.4 Unsupervised spambot detection via graph clustering
  • 10.5 Toward an accurate detection of social spambots
  • 10.5.1 The digital DNA behavioral modeling technique
  • 10.5.1.1 Digital DNA sequences
  • 10.5.1.2 Definition of digital DNA
  • 10.5.1.3 Similarity between digital DNA sequences
  • 10.6 DNA-inspired detection of social spambots
  • 10.6.1 LCS curves of legitimate and malicious accounts
  • 10.6.1.1 LCS curves of a group of homogeneous accounts
  • 10.6.1.2 LCS curves of a group of heterogeneous accounts
  • 10.6.2 An unsupervised detection technique
  • 10.6.3 Comparison with state-of-the-art detection techniques
  • 10.7 Conclusions and future directions
  • References

Inspec keywords: security of data; DNA; unsolicited e-mail; pattern classification; social networking (online)

Other keywords: users; DNA-inspired characterization; nature-inspired behavioral model; modeling online user behaviors; unsupervised detection approach; trolls; public opinion; spambot detection; supervised spambot detectors; advanced features; sensitive information; biological DNA; malicious accounts; social Twitter spambots

Subjects: Combinatorial mathematics; Other topics in statistics; Optimisation techniques; Data handling techniques; Image recognition; Information networks; Biology and medical computing; Data security; Neural computing techniques

Preview this chapter:
Zoom in
Zoomout

DNA-inspired characterization and detection of novel social Twitter spambots, Page 1 of 2

| /docserver/preview/fulltext/books/sc/pbse010e/PBSE010E_ch10-1.gif /docserver/preview/fulltext/books/sc/pbse010e/PBSE010E_ch10-2.gif

Related content

content/books/10.1049/pbse010e_ch10
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address