http://iet.metastore.ingenta.com
1887

AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning

AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning

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:
 
 
 
 
 
Big Data Recommender Systems - Volume 2: Application Paradigms — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In recent days, botnet plays an important role in malware distribution. This has been used as a primary approach for the proliferation of the malicious activities via the internet by attackers. To evade blacklisting, recent botnets make use of domain flux or internet protocol (IP) flux. This work focuses on domain flux. Domain flux uses domain generation algorithms (DGAs) to generate a list of domain names based on a seed and these domain names contacts command and control (C&C) server till it gets access permission to the system. This work presents the fully labeled domain name data set entitled as AmritaDGA which can be used for doing research in the field of detecting domain names which are generated using DGAs. We evaluate the efficacy of deep learning architectures with Keras embedding as domain name representation method on AmritaDGA. AmritaDGA is composed of two data sets. The first data set is collected from the publicly available sources. The second data set is collected from an internal real-time network. The performance of the trained model on public data set is evaluated on unseen samples of a public data set and private corpora. Deep learning architectures performed well in most of the cases of test experiments. The baseline system has been made publicly available and the data set is distributed for Detecting Malicious Domain names (DMD 2018) shared task.

Chapter Contents:

  • 22.1 Introduction
  • 22.2 Related methods toward deep learning-based DGA detection and categorization
  • 22.3 Summary of submitted systems of DMD 2018 shared task
  • 22.4 Domain name system (DNS)
  • 22.5 Domain fluxing
  • 22.6 Scalable framework
  • 22.7 Real-time DNS data collection in an Ethernet LAN
  • 22.8 Description of data set
  • 22.9 Deep learning
  • 22.9.1 Recurrent structures
  • 22.9.2 Convolutional neural network
  • 22.10 AmritaDGANet
  • 22.11 AmritaDGA data analysis, results and observations
  • 22.12 Conclusion and future work
  • Acknowledgments
  • References

Inspec keywords: neural nets; real-time systems; learning (artificial intelligence); Internet; invasive software

Other keywords: internet protocol flux; DGA; command and control server; IP flux; domain flux; botnet; blacklisting; Keras embedding; domain generation algorithms; access permission; deep learning; publicly available sources; malware distribution; malicious activities; Internet; domain name detection systems; AmritaDGA; internal real-time network

Subjects: Data security; Information networks; Neural computing techniques

Preview this chapter:
Zoom in
Zoomout

AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch22-1.gif /docserver/preview/fulltext/books/pc/pbpc035g/PBPC035G_ch22-2.gif

Related content

content/books/10.1049/pbpc035g_ch22
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address