Employing machine learning algorithms to detect unknown scanning and email worms

Shubair Abdulla, Sureswaran Ramadass, Altyeb Altaher, Amer Al-Nassiri

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


We present a worm detection system that leverages the reliability of IP-Flow and the effectiveness of learning machines. Typically, a host infected by a scanning or an email worm initiates a significant amount of traffic that does not rely on DNS to translate names into numeric IP addresses. Based on this fact, we capture and classify NetFlow records to extract feature patterns for each PC on the network within a certain period of time. A feature pattern includes: No of DNS requests, no of DNS responses, no of DNS normals, and no of DNS anomalies. Two learning machines are used, K-Nearest Neighbors (KNN) and Naive Bayes (NB), for the purpose of classification. Solid statistical tests, the cross-validation and paired t-test, are conducted to compare the individual performance between the KNN and NB algorithms. We used the classification accuracy, false alarm rates, and training time as metrics of performance to conclude which algorithm is superior to another. The data set used in training and testing the algorithms is created by using 18 real-life worm variants along with a big amount of benign flows.

Original languageEnglish
Pages (from-to)140-148
Number of pages9
JournalInternational Arab Journal of Information Technology
Issue number2
Publication statusPublished - Mar 2014


  • Email worms
  • IP-Flow
  • KNN
  • NB
  • Netflow
  • Scanning worms

ASJC Scopus subject areas

  • Computer Science(all)

Cite this