Online traffic classification for malicious flows using efficient machine learning techniques

Ying Yenn Chan, Ismahani Bt Ismail, Ban Mohammed Khammas

Abstract


The rapid network technology growth causing various network problems, attacks are becoming more sophisticated than defenses. In this paper, we proposed traffic classification by using machine learning technique, and statistical flow features such as five tuples for the training dataset. A rule-based system, Snort is used to identify the severe harmfulness data packets and reduce the training set dimensionality to a manageable size. Comparison of performance between training dataset that consists of all priorities malicious flows with only has priority 1 malicious flows are done. Different machine learning (ML) algorithms performance in terms of accuracy and efficiency are analyzed. Results show that Naïve Bayes achieved accuracy up to 99.82% for all priorities while 99.92% for extracted priority 1 of malicious flows training dataset in 0.06 seconds and be chosen to classify traffic in real-time process. It is demonstrated that by taking just five tuples information as features and using Snort alert information to extract only important flows and reduce size of dataset is actually comprehensive enough to supply a classifier with high efficiency and accuracy which can sustain the safety of network.

Keywords


machine learning; malicious traffic flows; online classification; snort alerts; statistical features;

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v19i4.20402

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120
Fax: +62 274 564604

View TELKOMNIKA Stats