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PANG, S. KOMOSNÝ, D. ZHU, L. ZHANG, R. SARRAFZADEH, A. BAN, T. INOUE, D.
Original Title
Malicious Events Grouping via Behavior Based Darknet Traffic Flow Analysis
Type
journal article in Web of Science
Language
English
Original Abstract
This paper proposes a host behavior based darknet traffic decomposition approach to identifying groups of malicious events from massive historical darknet traffic. In this approach, we segment traffic flows from captured darknet data, distinguish scan from non-scan flows, and categorize scans according to scan width spreads. Consequently, event groups are appraised by applying the criterion that malicious events generated by the same attacker or malicious software should have similar average packet delay, AvgDly. We have applied the proposed approach to 12 months darknet traffic data for malicious events grouping. As a result, several large scale event groups are discovered on host behavior in the category of port scan, IP scan and hybrid scan, respectively.
Keywords
Darknet traffic; Malicious events grouping; Port scan; IP scan; Hybrid scan; Packet delay distribution; Traffic flow analysis
Authors
PANG, S.; KOMOSNÝ, D.; ZHU, L.; ZHANG, R.; SARRAFZADEH, A.; BAN, T.; INOUE, D.
Released
13. 10. 2017
ISBN
0929-6212
Periodical
WIRELESS PERSONAL COMMUNICATIONS
Year of study
96
Number
4
State
Kingdom of the Netherlands
Pages from
5335
Pages to
5353
Pages count
19
BibTex
@article{BUT141089, author="Shaoning {Pang} and Dan {Komosný} and Lei {ZHU} and Ruibin {ZHANG} and Abdolhossein {SARRAFZADEH} and Tao {Ban} and Daisuke {Inoue}", title="Malicious Events Grouping via Behavior Based Darknet Traffic Flow Analysis", journal="WIRELESS PERSONAL COMMUNICATIONS", year="2017", volume="96", number="4", pages="5335--5353", doi="10.1007/s11277-016-3744-4", issn="0929-6212" }