A deep density based and self-determining clustering approach to label unknown traffic

Mehrnoosh Monshizadeh*, Vikramajeet Khatri, Raimo Kantola, Zheng Yan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
37 Downloads (Pure)

Abstract

Analyzing non-labeled data is a major concern in the field of intrusion detection as the attack clusters are continuously evolving which are unknown for the system. Many studies have been conducted on different techniques such as clustering to solve this issue. Consequently, in this paper the clustering techniques are applied based on the packets’ similarity to categorize unknown traffic. After clustering is done by the proposed architecture, the security investigator analyzes one packet from each cluster (instead of thousands of packets) and generalize the result of analysis to all packets belonging to the cluster. The proposed architecture, namely Associated Density Based Clustering (ADBC) applies multiple unsupervised algorithms and a co-association matrix to detect attack clusters of any shape as long as they have density-connected elements. Furthermore, the architecture automatically determines the best number of clusters in order to categorize non-labeled data. The performance of proposed architecture is evaluated based on the various metrics, while its generalization capability is tested with three publicly available datasets.

Original languageEnglish
Article number103513
Number of pages18
JournalJournal of Network and Computer Applications
Volume207
DOIs
Publication statusPublished - Nov 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Data mining
  • Intrusion detection
  • Machine Learning
  • Network security
  • Network traffic

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