This is an outdated version published on 2017-06-25. Read the most recent version.

A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications

Authors

  • Aun Yichiet National Advance IPv6 Centre of Excellence; Universiti Sains Malaysia
  • Selvakumar Manickam NAv6, USM, Malaysia
  • Shankar Karuppayah NAv6, USM, Malaysia

DOI:

https://doi.org/10.17762/ijcnis.v9i2.2368

Abstract

Mobile traffics are becoming more dominant due to growing usage of mobile devices and proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic classifications; namely the diversity complexity and behavioral dynamism complexity. Existing traffic classifications methods are designed for classifying standard protocols and user applications with more deterministic behaviors in small diversity. Currently, flow statistics, payload signature and heuristic traffic attributes are some of the most effective features used to discriminate traffic classes. In this paper, we investigate the correlations of these features to the less-deterministic user application traffic classes based on corresponding classification accuracy. Then, we evaluate the impact of large-scale classification on feature's robustness based on sign of diminishing accuracy. Our experimental results consolidate the needs for unsupervised feature learning to address the dynamism of mobile application behavioral traits for accurate classification on rapidly growing mobile traffics.

Author Biographies

Aun Yichiet, National Advance IPv6 Centre of Excellence; Universiti Sains Malaysia

PhD candidate in NAv6, USM, MalaysiaArea of interests include big data analytics, machine learning, and IoT indexing.

Selvakumar Manickam, NAv6, USM, Malaysia

Shankar Karuppayah, NAv6, USM, Malaysia

Downloads

Published

2017-06-25

Versions

How to Cite

Yichiet, A., Manickam, S., & Karuppayah, S. (2017). A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications. International Journal of Communication Networks and Information Security (IJCNIS), 9(2). https://doi.org/10.17762/ijcnis.v9i2.2368

Issue

Section

Surveys / Reviews