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SNTA 2018: Scalable Network Traffic Analytics

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Submission Date:
2018-04-04 Extended
Notification Date:
2018-04-11
Conference Date:
2018-07-02
Location:
Vienna, Austria
Viewed: 12090   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

42.1 / 100
Ranked #4,126 of 5,680 conferences · Top 73%
Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
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Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-11

Call For Papers

SNTA 2018 (Scalable Network Traffic Analytics) is an academic conference held in Vienna, Austria on 2018-07-02. The paper submission deadline is 2018-04-04 (extended). Acceptance notifications are sent on 2018-04-11.

Network traffic processing and analysis tasks are a key element for operations and management in distributed systems, which have become more challenging due to increasing traffic volumes and dynamic traffic data characteristics. The tasks will be significantly complicated with the greater network speed and the newly introduced mobile and IoT devices within the next few years. Such changes will render the existing traffic analysis techniques to be outdated, and scalable big data analytics may be in place for data-driven and deeper data analysis. In addition to the quantitative and qualitative challenges, big data in network traffic analysis also comes from various sources such as routers, firewalls, intrusion sensors, and the newly emerging network elements speaking with different syntax and semantics, which makes organizing and incorporating the generated data difficult for comprehensive analysis. This workshop aims at bridging the network traffic processing and the latest advances in machine learning and data science technologies. New analysis techniques for network traffic data are needed in the big data era, from the diverse angles of network performance, availability, and security. For example, real-time streaming analytics algorithms and methods need to be explored for estimating network performance and summarizing the traffic variables to capture the network activities due to the network bandwidth increase. Multivariate analysis of traffic variables may be able to provide the intuitive, comprehensive view of the traffic dynamics. New network activities logging techniques are also needed in the future with the latest development in the storage and archival technologies. Many applications in network traffic analysis may need to address the application-specific requirements and challenges. In this workshop, we intend to share visions of investigating new approaches and methods at the intersection of networking and data sciences. Topics of interest include in the workshop, but are not limited to: Network traffic summarization Data-driven, multivariate traffic analysis Streaming-based data processing and analysis Big data analytics for traffic analysis Network forensic technologies Capturing, filtering, representing traffic data Storage and archival technologies for traffic data management Traffic analysis applications including change detection and anomaly detection Algorithms, frameworks, tools, and services for traffic analytics Experiences, practices, and measurement studies on network traffic analysis Network traffic characteristics and measurement Visualization of network traffic and attack information IoT and wireless network traffic analysis
Last updated by Dou Sun on

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