Metrics and Techniques for Early Detection in Communication Networks

Nowadays, communication networks have a high relevance in any field. Because of this, it is necessary to maintain them working properly and with an adequate security level. In many fields, and in anomaly detection in communication networks in particular, it results really convenient the use of early detection methods. Therefore, adequate metrics must be defined to allow the correct evaluation of methods applied in relation to time delay in the detection. In this thesis the definition of time-aware metrics for early detection anomaly techniques evaluation.

[1]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[2]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[3]  Benoit Claise,et al.  Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information , 2008, RFC.

[4]  A. Fuggetta,et al.  Feature engineering [software development] , 1998, Proceedings Ninth International Workshop on Software Specification and Design.

[5]  Steven Bethard,et al.  Measuring the Latency of Depression Detection in Social Media , 2018, WSDM.

[6]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[7]  Zheng Yan,et al.  A survey on network data collection , 2018, J. Netw. Comput. Appl..

[8]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[9]  Benoit Claise,et al.  Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of Flow Information , 2013, RFC.

[10]  Nancy Chinchor,et al.  MUC-4 evaluation metrics , 1992, MUC.

[11]  Mohiuddin Ahmed,et al.  A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..

[12]  Muhammad Sher,et al.  Flow-based intrusion detection: Techniques and challenges , 2017, Comput. Secur..

[13]  Jinoh Kim,et al.  A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.

[14]  Fabio Crestani,et al.  A Test Collection for Research on Depression and Language Use , 2016, CLEF.

[15]  Zheng Yan,et al.  A Survey on Network Security-Related Data Collection Technologies , 2018, IEEE Access.