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Hybrid Intrusion Detection System for Worm Attacks Based on Their Network Behavior

  • Hassan Hadi Latheeth AL-MaksousyEmail author
  • Michele C. Weigle
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 259)

Abstract

Computer worms are characterized by rapid propagation and intrusive network disruption. In this work, we analyze the network behavior of five Internet worms: Sasser, Slammer, Eternal Rocks, WannaCry, and Petya. Through this analysis, we use a deep neural network to successfully classify network traces of these worms along with normal traffic. Our hybrid approach includes a visualization that allows for further analysis and tracing of the network behavior of detected worms.

Keywords

Deep learning Worm traffic Internet worms Sasser Slammer NotPetya WannaCry EternalRocks Visualization 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Hassan Hadi Latheeth AL-Maksousy
    • 1
    Email author
  • Michele C. Weigle
    • 1
  1. 1.Department of Computer ScienceOld Dominion UniversityNorfolkUSA

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