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Perception Mining of Network Protocol’s Stealth Attack Behaviors

  • Yan-Jing Hu
  • Xu An WangEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

Unknown network protocol’s stealth attack behavior is becoming a new type of attack, which greatly harms the cyber space security. The stealth behaviors are not easy to be detected by existing security measures. Starting with the implementation of the instructions of the protocol programs, the normal behavior instruction sequences are captured by dynamic binary analysis. The algorithm of instruction clustering and feature distance computation is designed to mine the potential stealth attack behavior instruction sequences. The mined stealth attack behavior instruction sequences (for inline assembly) are loaded into the general executing framework. A virtual protocol behavior analysis platform HiddenDisc has been developed, and the Dynamic analysis is implemented on the platform. Then the protocol execution security evaluation scheme is proposed and implemented. Using the stealth transformation method designed by ourselves, the stealth attack behaviors are transformed. We successfully attacked the virtual target machine by using the transformed stealth attack behaviors, but the stealth behaviors were not captured. The experimental results show that the present method can accurately and efficiently perception mining unknown protocol’s stealth attack behaviors, transform and use of stealth attack behavior can also enhance our information offensive and defensive capabilities.

Keywords

Protocol reverse Stealth attack behavior Instruction clustering 

Notes

Acknowledgements

This work is supported by the National Key Research and Development Program of China Under Grants No. 2017YFB0802000, National Cryptography Development Fund of China Under Grants No. MMJJ20170112, the Natural Science Basic Research Plan in Shaanxi Province of china (Grant Nos. 2018JM6028), National Nature Science Foundation of China (Grant Nos. 61772550, 61572521, U1636114, 61402531, 61103178, 61373170, 61402530, 61309022 and 61309008.), Engineering University of PAP’s Funding for Scientific Research Innovation Team (grant no. KYTD201805).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Network and Information Security Key LaboratoryEngineering University of the Armed Police ForceXi’anChina
  2. 2.National Key Laboratory of Integrated Services NetworksXidian UniversityXi’anChina

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