IEA/AIE NAAD 2017 : Special Track on Novel Approaches to Anomaly Detection
Call For Papers
IEA/AIE 2017: Special Track on Novel Approaches to Anomaly Detection
We invite submissions of the latest research on approaches, applications (both academic and industrial), and theories in the area of anomaly detection to the Special Track on Novel Approaches to Anomaly Detection which is at the 30th International
Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE http://www.cril.univ-artois.fr/ieaaie2017/ ).
Submitted manuscripts should be original unpublished work and not be submitted to any other regular or special sessions, or under consideration elsewhere. The manuscripts should be a maximum of 10 pages in length, however work in progress papers may be shorter.
All submissions will all be peer-reviewed and accepted papers will be included in the main conference proceedings. This will be published in a bound volume by Springer-Verlag (formatting instructions are available at http://www.springer.de/comp/lncs/authors.html) in their Lecture Notes in Artificial Intelligence series.
Selected papers from this special track will be invited to be further extended for a Special Issue of the Journal of Data Science and Analytics, published by Springer (http://www.springer.com/computer/database+management+%26+information+retrieval/journal/41060).
Please submit your paper using the main conference EasyChair (https://easychair.org/conferences/?conf=ieaaie2017 ) and select the Anomaly Detection special track.
We invite submissions of Novel Approaches to Anomaly Detection. Specific topics of interest include, but are not limited to:
• Anomaly detection on graph data.
• Anomaly detection on high performance and distributed systems (HPDC).
• Anomaly detection on image / video data.
• Anomaly detection on large scale or 'Big Data'.
• Anomaly detection on sequential data.
• Anomaly detection on time series data.
• Anomaly detection with Deep Learning.
• Anomaly detection with high dimensions.
• Concept drift in anomaly detection.
• Conditional anomaly detection.
• Contextual anomaly detection.
• Ensemble methods for anomaly detection.
• Online anomaly detection.
• Optimisation methods for anomaly detection.
• Probabilistic anomaly detection.
• Real time anomaly detection.
• Spatio-temporal anomaly detection.
• Streaming anomaly detection.
• Subspace anomaly detection.
• Anomaly detection in industry e.g.
◦ Operations (IT etc.)
◦ Aviation and vehicles
• Applications of anomaly detection e.g.
◦ Financial fraud
◦ Sensor networks
◦ Network security
◦ Behaviour analysis (online / offline)
◦ Event detection
◦ Image processing / Video analytics
Submissions: December 15, 2016 (deadline extended)
Acceptance: January 15 2017
Camera Ready: February 15 2017
Conference: June 27 - 30 2017
Weiru Liu (Queen's University Belfast)
Ryan McConville (Queen's University Belfast)