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A Deep Learning Based Online Malicious URL and DNS Detection Scheme

  • Jianguo Jiang
  • Jiuming Chen
  • Kim-Kwang Raymond Choo
  • Chao Liu
  • Kunying Liu
  • Min Yu
  • Yongjian Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 238)

Abstract

URL and DNS are two common attack vectors in malicious network activities; thus, detection for malicious URL and DNS is crucial in network security. In this paper, we propose an online detection scheme based on character-level deep neural networks. Specifically, this scheme maps the URL and DNS strings into vector form using some natural language processing methods. The CNN (Convolutional Neural Network) network framework is then designed to automatically extract the malicious features and train the classifying model. Experimental results on real-world URL and DNS datasets show that proposed method outperforms several state-of-art baseline methods, in terms of efficiency and scalability.

Keywords

Network security Malicious URL detection Online detection CNN 

Notes

Acknowledgment

This work is supported by National Natural Science Foundation of China (No. 61173008, 61402124), Strategic Pilot Technology Chinese Academy of Sciences (No. XDA06010703) and Key Lab of Information Network Security, Ministry of Public Security (No. C17614).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Jianguo Jiang
    • 1
  • Jiuming Chen
    • 1
    • 2
  • Kim-Kwang Raymond Choo
    • 3
  • Chao Liu
    • 1
  • Kunying Liu
    • 1
  • Min Yu
    • 1
    • 2
  • Yongjian Wang
    • 4
  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Information Systems and Cyber SecurityUniversity of Texas at San AntonioSan AntonioUSA
  4. 4.Key Laboratory of Information Network Security of Ministry of Public SecurityThe Third Research Institute of Ministry of Public SecurityShanghaiChina

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