The telecommunications illustrated dictionary, 2002. ,
On the Detection of Signaling DoS Attacks on 3G Wireless Networks, IEEE INFOCOM 2007, 26th IEEE International Conference on Computer Communications, pp.1289-1297, 2007. ,
DOI : 10.1109/INFCOM.2007.153
On the detection of signaling DoS attacks on 3G/WiMax wireless networks, Computer Networks, vol.53, issue.15, pp.2601-2616, 2009. ,
DOI : 10.1016/j.comnet.2009.05.008
A distribution-based approach to anomaly detection and application to 3G mobile traffic, Global Telecommunications Conference, pp.1-8, 2009. ,
Distribution-Based Anomaly Detection in Network Traffic, Data Traffic Monitoring and Analysis, pp.202-216, 2013. ,
DOI : 10.1007/978-3-642-36784-7_9
SMS-Watchdog: Profiling Social Behaviors of SMS Users for Anomaly Detection, Recent Advances in Intrusion Detection, pp.202-223, 2009. ,
DOI : 10.1007/978-3-642-04342-0_11
A Detection Mechanism for SMS Flooding Attacks in Cellular Networks, Security and Privacy in Communication Networks, pp.76-93, 2013. ,
DOI : 10.1145/1541880.1541882
Anomaly detection in cellular Machine-to-Machine communications, 2013 IEEE International Conference on Communications (ICC), pp.2138-2143, 2013. ,
DOI : 10.1109/ICC.2013.6654843
Reality mining: sensing complex social systems, Personal and ubiquitous computing, pp.255-268, 2006. ,
DOI : 10.1007/s00779-005-0046-3
Cell phone mini challenge award: Social network accuracyexploring temporal communication in mobile call graphs, Visual Analytics Science and Technology VAST'08. IEEE Symposium on, 2008. ,
MobiVis: A Visualization System for Exploring Mobile Data, 2008 IEEE Pacific Visualization Symposium, pp.175-182, 2008. ,
DOI : 10.1109/PACIFICVIS.2008.4475474
LOF: identifying densitybased local outliers, ACM Sigmod Record, pp.93-104, 2000. ,
Visual Analytics for Enhancing Supervised Attack Attribution in Mobile Networks, Information Sciences and Systems 2014, pp.193-203, 2014. ,
DOI : 10.1007/978-3-319-09465-6_21
Multi-Objective Optimization for Multimodal Visualization, IEEE Transactions on Multimedia, vol.16, issue.5, pp.1460-1472, 2014. ,
DOI : 10.1109/TMM.2014.2316473
Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001. ,
DOI : 10.1023/A:1010933404324
Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database, 2009 IEEE 25th International Conference on Data Engineering, pp.1024-1035, 2009. ,
DOI : 10.1109/ICDE.2009.104
GEDIS Studio online, 2014. ,