.png){kind=icon}
.png){kind=icon}
Return
SuperSketch: A Multi-Dimensional Reversible Data Structure for Super Host Identification
.png){kind=icon}
OA
AI
DOI：10.1109/TDSC.2021.3072295.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Facing big network traffic data, effective data compression becomes crucially important and urgently needed for estimating host cardinalities and identifying super hosts. However, the current literature confronts several challenges: incapability of simultaneously measuring various types of host cardinalities and inability to efficiently reconstruct super host addresses. To address these challenges, in this article, we propose a novel sketch data structure, named SuperSketch, to simultaneously measure multiple types of host cardinalities with the purpose of efficiently identifying super hosts. SuperSketch has two significant characteristics: multi-dimensionality and reversibility. The multi-dimensionality makes SuperSketch capable of simultaneously measuring Source Cardinality, Destination Cardinality, and Destination Port Cardinality. The reversibility allows SuperSketch to accurately and quickly reconstruct the original addresses of super hosts once they are identified. We conduct both theoretical analysis and performance evaluation based on real-world network traffic. Experimental results show that SuperSketch achieves outstanding performance for multi-cardinality measurement, super host identification, and host address reconstruction.
Keywords:
Monitoring
Data structures
Estimation
Time measurement
Task analysis
Hash functions
Size measurement
Network traffic measurement
host cardinality
reversible sketch
super host identification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.4K
Citations:
9.6K