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A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model

Author

Listed:
  • Enbo Sun

    (The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610041, China)

  • Jiaxuan Han

    (School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China)

  • Yiquan Li

    (The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610041, China)

  • Cheng Huang

    (School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China)

Abstract

In recent years, various Remote Code Execution vulnerabilities on the Internet have been exposed frequently; thus, more and more security researchers have begun to pay attention to the detection of Remote Code Execution attacks. In this paper, we focus on three kinds of common Remote Code Execution attacks: XML External Entity, Expression Language Injection, and Insecure Deserialization. We propose a packet content-oriented Remote Code Execution attack payload detection model. For the XML External Entity attack, we propose an algorithm to construct the use-definition chain of XML entities, and implement detection based on the integrity of the chain and the behavior of the chain’s tail node. For the Expression Language Injection and Insecure Deserialization attack, we extract 34 features to represent the string operation and the use of sensitive classes/methods in the code, and then train a machine learning model to implement detection. At the same time, we build a dataset to evaluate the effect of the proposed model. The evaluation results show that the model performs well in detecting XML External Entity attacks, achieving a precision of 0.85 and a recall of 0.94. Similarly, the model performs well in detecting Expression Language Injection and Insecure Deserialization attacks, achieving a precision of 0.99 and a recall of 0.88.

Suggested Citation

  • Enbo Sun & Jiaxuan Han & Yiquan Li & Cheng Huang, 2024. "A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model," Future Internet, MDPI, vol. 16(7), pages 1-18, July.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:7:p:235-:d:1427271
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    References listed on IDEAS

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    1. Fei Xiao & Zhaowen Lin & Yi Sun & Yan Ma, 2019. "Malware Detection Based on Deep Learning of Behavior Graphs," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, February.
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