Power-System IOTIPS Protocol Fuzz Testing for Vulnerability Detection
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Solution Overview
Problem
Existing methods are inadequate for effectively detecting vulnerabilities in the industrial control communication protocols of Internet of Things in Power Systems (IOTIPS), posing a significant threat to the security and stability of industrial control systems.
Innovation Solution
A system and method for detecting vulnerabilities in IOTIPS protocols using fuzz testing, involving protocol configuration, sample generation, and execution monitoring, with customizable mutation strategies and secondary learning via a Seq2Seq model with Attention mechanism to enhance detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used for IOTIPS protocol vulnerability detection, then the testing process is simple, but the detection effectiveness and capability to identify protocol vulnerabilities are insufficient
Solution Approach 1:
The testing system is segmented into three independent modules: protocol configuration module, sample generation module, and execution monitoring module. Each module handles specific tasks (protocol definition, test case generation with mutation strategies, and test execution with result analysis), allowing the complex vulnerability detection functionality to be distributed across modular components that can be developed, maintained, and configured independently while achieving comprehensive protocol vulnerability detection
Solution Approach 2:
The protocol configuration module performs preliminary actions by defining the protocol structure, data frames, and communication rules before the actual vulnerability detection begins. This pre-configuration establishes the baseline protocol behavior that the execution monitoring module will compare against during testing, enabling systematic vulnerability detection without requiring complex real-time analysis
2Measurement precision
If fuzz testing with mutation strategies is implemented, then the vulnerability detection accuracy improves, but the testing time and computational resources increase
Solution Approach 1:
The sample generation module implements partial action by applying mutation strategies selectively to specific data frames and protocol fields based on their vulnerability risk profiles. Rather than mutating every possible byte in every protocol message, the system focuses mutation efforts on critical fields (e.g., command parameters, data lengths, checksums) where vulnerabilities are most likely to exist, thereby maintaining high detection accuracy while reducing overall testing time
Solution Approach 2:
The execution monitoring module implements feedback mechanisms by analyzing test results in real-time and adjusting the testing strategy accordingly. When abnormal responses or crashes are detected, the system feeds back this information to the sample generation module to intensify mutation efforts on similar protocol structures, while skipping redundant testing on already-validated paths, thus optimizing the balance between detection accuracy and testing time
Data Source
AI summary
Disclosed are a system and method for detecting a vulnerability of an Internet of Things in Power Systems (IOTIPS) protocol based on fuzz testing. The system includes: a protocol configuration module configured to configure a protocol to be detected and a data frame of the protocol to be detected; a sample generation module configured to: after loading the protocol and the data frame configured by the protocol configuration module, configure a mutation strategy based on the loaded protocol, and generate a test sample based on the configured mutation strategy; and an execution monitoring module configured to transfer the test sample generated by the sample generation module to a device to be detected for testing, perform link management and test execution, store a test result, and generate a detection report.