Protocol State Fuzzing for SDN Control Plane Security
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Solution Overview
Problem
The security of East-West interfaces in software-defined network (SDN) controller clusters remains unexamined, posing significant risks as attackers can exploit vulnerabilities to control clusters, disrupt networks, or obtain sensitive information, with traditional test tools being inadequate for distributed SDN environments.
Innovation Solution
A protocol state fuzzing method and system, implemented through an ambusher, which passively and actively monitors intercommunication between network operating system (NOS) instances, generates possible attack scenarios, and infers operation states by converting input alphabets into protocol messages, using a state machine builder, fuzzer, and cluster environment manager to explore system states and detect vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional test tools are used to examine security, then the testing process is simple and familiar, but they are inadequate for distributed SDN environments and cannot effectively discover vulnerabilities in East-West interfaces
Solution Approach 1:
The test tool is divided into multiple specialized modules: a state machine builder for protocol modeling, a fuzzer for generating test inputs, a cluster environment manager for distributed system coordination, and a passive/active monitoring component. Each module handles a specific aspect of security testing, making the complex task of examining distributed SDN East-West interfaces manageable and effective
Solution Approach 2:
The patent introduces an ambusher as an intermediary testing system that sits between test inputs and the distributed SDN cluster. This ambusher acts as a mediator that translates high-level test cases into protocol-specific messages for East-West interfaces, enabling effective security examination without requiring direct modification of the cluster architecture
2Reliability
If protocol state fuzzing is implemented to discover vulnerabilities, then security coverage is improved, but the testing process requires complex state machine modeling and protocol message conversion
Solution Approach 1:
The state machine builder performs preliminary action by automatically modeling the protocol specification into a state machine representation before fuzzing begins. This pre-processing step captures all valid protocol states and transitions, enabling the fuzzer to generate meaningful test cases without manually crafting complex protocol messages during the actual testing phase
Solution Approach 2:
The testing system performs self-service through automated protocol message conversion. The fuzzer automatically converts generated test inputs into appropriate protocol messages based on the modeled state machine, and the cluster environment manager automatically manages the distributed cluster setup and coordination, reducing the need for manual intervention in complex protocol-specific details
3Loss of information
If passive and active monitoring is used to infer operation states, then attack scenarios are revealed, but the monitoring and analysis process becomes more complex
Solution Approach 1:
The monitoring system merges passive monitoring (observing existing traffic) and active monitoring (injecting test messages) into a unified ambusher framework. Both monitoring modes feed into the same state inference engine, which combines observations to build and update the state machine model, revealing attack scenarios through integrated analysis rather than separate processes
Data Source
AI summary
A protocol state fuzzing method for security of a control plane of a distributed software-defined network is provided. The protocol state fuzzing method includes receiving input alphabets being abstract symbols of a protocol message in an ambusher of a distributed network operating system (NOS), converting the input alphabets into the protocol message, and sending the protocol message to a cluster, monitoring, by the cluster, intercommunication between instances in the distributed NOS, and selecting a set of sequences executable in the cluster and searching a cluster log for an output by executing the sequence to generate an attack result.