An intelligent defense and filtration platform for network traffic

Mehrnoosh Monshizadeh*, Vikramajeet Khatri, Buse Atli, Raimo Kantola

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


Hybrid Anomaly Detection Model (HADM) is a security platform to detect and prevent cyber-attacks on communication networks. The platform uses a combination of linear and learning algorithms combined with protocol analyzer. The linear algorithms filter and extract distinctive attributes and features of the cyber-attacks while the learning algorithms use these attributes and features to identify new types of cyber-attacks. The protocol analyzer in this platform classifies and filters vulnerable protocols to avoid unnecessary computation load. The use of linear algorithms in conjunction with learning algorithms allows the HADM to achieve improved efficiency in terms of accuracy and computation time in order to detect cyber-attacks over existing solutions.

Original languageEnglish
Title of host publicationWired/Wireless Internet Communications - 16th IFIP WG 6.2 International Conference, WWIC 2018, Proceedings
EditorsKaushik Roy Chowdhury, Marco Di Felice, Bo Sheng, Ibrahim Matta
Number of pages12
ISBN (Electronic)978-3-030-02931-9
ISBN (Print)978-3-030-02930-2
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Wired/Wireless Internet Communications - Boston, United States
Duration: 18 Jun 201820 Jun 2018
Conference number: 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10866 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Wired/Wireless Internet Communications
Abbreviated titleWWIC
Country/TerritoryUnited States


  • Anomaly detection
  • Cloud computing
  • Internet of things
  • Machine learning
  • Security


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