Machine learning, data mining and Big Data frameworks for network monitoring and troubleshooting
The complexity of the Internet has dramatically increased in the last few years, making it more important and challenging to design scalable network traffic monitoring and analysis applications and tools. Critical applications such as detection of anomalies, network attacks and intrusions, require fast mechanisms for online analysis of thousands of events per second, as well as efficient techniques for offline analysis of massive historical data. Besides recent major advances of Big Data analysis frameworks, their application to the network traffic monitoring and analysis domain remains poorly understood and investigated. Furthermore, data mining and machine learning-based techniques able to detect, characterize, and troubleshoot network anomalies and security incidents, promise to efficiently shed light on this enormous amount of data.
The purpose of this special issue is to bring together state-of-the-art studies by researchers and practitioners that describe original and unpublished work with respect to novel scalable techniques and frameworks capable of collecting and analyzing both online streams and offline massive datasets, of network traffic traces, topological data, and performance measurements. Special consideration will be reserved for works sharing software and/or datasets with the research community.
Extended version of the two best papers on related topics, accepted at the 8th International Workshop Traffic Monitoring and Analysis (Workshop URL), will be granted fast track for publication in the Special Issue, subject to further revision.
Topics of interest:
Topics of interest include, but are not limited to the following:
Big Data analysis frameworks for network monitoring data
Application of Big Data analysis frameworks to traffic monitoring and analysis
Benchmarks for Big Data analysis solutions
Machine learning, data mining algorithms and Big Data analytics
Algorithms and tools for network anomaly and security threats detection
Network anomalies diagnosis and troubleshooting
Expert systems and decision support systems for network troubleshooting
Measurement based network management
Network monitoring systems and distributed monitoring architectures
Measurements related to data centers and cloud-based services
Collection and processing systems for large-scale topology and performance measurements
Creative papers outside of the areas enumerated here are also welcome. Prospective authors can contact the Guest Editors regarding not listed topics.
All received submissions will be sent out for peer review by three experts in the field and be evaluated with respect to the relevance to this special issue, level of innovation, depth of contribution, and quality of presentation. The Guest Editors will make an initial determination of the suitability and scope for all submissions. Papers that either lack originality or clarity in presentation will not be sent for review and the authors will be promptly informed in such cases. Submitted papers must not be under consideration by other journals, publications or conferences.
The submission must be clearly written and in excellent English, with a maximum page limit of 20 pages. If the paper was published in a conference, the submitted manuscript must be a substantial extension of the conference paper. In this case, authors are also required to submit a summary document explaining the enhancements made in the journal version.
Authors should follow the Computer Networks manuscript format described below at the journal site: http://www.elsevier.com/journals/computer-networks/1389-1286/guide-for-authors.
Manuscripts should be submitted online through the Elsevier Editorial System http://ees.elsevier.com/comnet/ and authors should select “SI: Network Monitoring” as the article type for submission.
Paper Submission Due: October 30, 2015
First round review results: January 29 2016
Submission of revised papers: March 11, 2016
Second round review results: April 29, 2016
Publication Target Date: Mid 2016
Dr Alessandro D’Alconzo, FTW Forschungszentrum Telekommunikation Wien, Austria, [email protected]
Prof Pere Barlet-Ros, UPC BarcelonaTech, Barcelona, Spain, [email protected]
Prof Kensuke Fukuda, National Institute of Informatics, Tokyo, Japan, [email protected]
Prof David Choffnes, Northeastern University, Boston, USA, [email protected]