ICEPRE: ICS Protocol Reverse Engineering via Data-Driven Concolic Execution

With the advancement of digital transformation, Industrial Control Systems (ICS) are becoming increasingly open and intelligent. However, inherent vulnerabilities in ICS protocols pose significant security threats to devices and systems. The proprietary nature of ICS protocols complicates the security analysis and deployment of protective mechanisms for ICS. Protocol reverse engineering aims to infer the syntax, semantics, and state machines of protocols in the absence of official specifications. Traditional protocol reverse engineering tools face considerable limitations due to the lack of executable environments, incomplete inference strategies, and low-quality network traffic. In this paper, we present ICEPRE, a novel data-driven protocol reverse engineering method based on concolic execution, which uniquely integrates network trace with static analysis. Unlike conventional methods that rely on executable environments, ICEPRE statically tracks the program's parsing process for specific input messages. Furthermore, we employ an innovative field boundary inference strategy to infer the protocol's syntax by analyzing how the protocol parser handles different fields. Our evaluation demonstrates that ICEPRE significantly outperforms previous protocol reverse engineering tools in field boundary inference, achieving an F1 score of 0.76 and a perfection score of 0.67, while DynPRE, BinaryInferno, Nemeys, and Netzob yield (0.65, 0.35), (0.42, 0.14), (0.39, 0.09), and (0.27, 0.10), respectively. These results underscore the superior overall performance of our method. Additionally, ICEPRE exhibits exceptional performance with proprietary protocols in real-world scenarios, highlighting its practical applicability in downstream applications.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-22
DOI
https://doi.org/10.1145/3728982
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

ICEPRE: ICS Protocol Reverse Engineering via Data-Driven Concolic Execution

Shuaizong Si, J. L. Cheng, Dongliang Fang, Yongle Chen et al.
Proceedings of the ACM on software engineering.
Advanced Malware Detection Techniques
article

ICEPRE: ICS Protocol Reverse Engineering via Data-Driven Concolic Execution

Shuaizong Si, J. L. Cheng, Dongliang Fang, Yongle Chen, Limin Sun, Zhen Wang, Y. Z. Qu
article en

Abstract

With the advancement of digital transformation, Industrial Control Systems (ICS) are becoming increasingly open and intelligent. However, inherent vulnerabilities in ICS protocols pose significant security threats to devices and systems. The proprietary nature of ICS protocols complicates the security analysis and deployment of protective mechanisms for ICS. Protocol reverse engineering aims to infer the syntax, semantics, and state machines of protocols in the absence of official specifications. Traditional protocol reverse engineering tools face considerable limitations due to the lack of executable environments, incomplete inference strategies, and low-quality network traffic. In this paper, we present ICEPRE, a novel data-driven protocol reverse engineering method based on concolic execution, which uniquely integrates network trace with static analysis. Unlike conventional methods that rely on executable environments, ICEPRE statically tracks the program's parsing process for specific input messages. Furthermore, we employ an innovative field boundary inference strategy to infer the protocol's syntax by analyzing how the protocol parser handles different fields. Our evaluation demonstrates that ICEPRE significantly outperforms previous protocol reverse engineering tools in field boundary inference, achieving an F1 score of 0.76 and a perfection score of 0.67, while DynPRE, BinaryInferno, Nemeys, and Netzob yield (0.65, 0.35), (0.42, 0.14), (0.39, 0.09), and (0.27, 0.10), respectively. These results underscore the superior overall performance of our method. Additionally, ICEPRE exhibits exceptional performance with proprietary protocols in real-world scenarios, highlighting its practical applicability in downstream applications.

Proceedings of the ACM on software engineering.Vol. 2(ISSTA)
Chinese Academy of Sciences (CN), Institute of Information Engineering (CN), University of Chinese Academy of Sciences (CN), Taiyuan University of Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 83%
Advanced Malware Detection Techniques
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