Classification of Malicious Traffic Using TensorFlow Machine Learning

Li Der Chou, Chia Wei Tseng, Meng Sheng Lai, Wei Yu Chen, Kuo Chung Chen, Chia Kuan Yen, Tsung Fu Ou, Wei Hsiang Tsai, Yi Hsuan Chiu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

With the rapid development of the Internet and the innovative attacks, information security has become an important issue for system administrators and users. Because the traditional intrusion detection system is based on misuse detection technology, the disadvantage is that it needs constant updating of the feature database to cope with attacks from variant malware. This paper proposes a framework of deep learning model by using the TensorFlow platform and utilizes the NSL-KDD data set for training and testing the proposed framework. Experimental results show the proposed methodology can effectively classify malicious traffic categories.

Original languageEnglish
Title of host publication9th International Conference on Information and Communication Technology Convergence
Subtitle of host publicationICT Convergence Powered by Smart Intelligence, ICTC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages186-190
Number of pages5
ISBN (Electronic)9781538650400
DOIs
StatePublished - 16 Nov 2018
Event9th International Conference on Information and Communication Technology Convergence, ICTC 2018 - Jeju Island, Korea, Republic of
Duration: 17 Oct 201819 Oct 2018

Publication series

Name9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018

Conference

Conference9th International Conference on Information and Communication Technology Convergence, ICTC 2018
Country/TerritoryKorea, Republic of
CityJeju Island
Period17/10/1819/10/18

Keywords

  • IDS
  • KDD Dataset
  • Machine Learning
  • TensorFlow
  • Traffic Classification

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