Semantic Communication Control Plane for Poisoning Attack Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems are susceptible to cyber-attacks through poisoning of machine learning models used in semantic communication, which can disrupt applications like autonomous driving and vehicle platooning, and there is a need for improved methods to detect and mitigate such attacks.
Innovation Solution
A control plane mechanism is introduced to enhance the robustness of semantic decoding by using semantic coding to detect attacks, employing semantic distortion indicators (SDI) and red flags, and implementing a Distributed Semantic Measurement and Control Entity (D-SMCE) to monitor and mitigate semantic distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used for semantic communication to improve communication efficiency, then communication efficiency is improved, but the system becomes vulnerable to poisoning attacks that can compromise security
Solution Approach 1:
The patent implements preliminary security measures by establishing a control plane mechanism before data plane operations. The system pre-trains semantic models with security-aware training data and pre-establishes monitoring capabilities through D-SMCE entities that can detect attacks before they compromise communication. This proactive approach allows the system to maintain both efficiency and security by having defensive mechanisms in place beforehand.
Solution Approach 2:
The patent introduces a control plane as an intermediary layer between the data plane and semantic models. This control plane includes D-SMCE entities that act as mediators to monitor, detect, and respond to poisoning attacks. The intermediary control plane separates the efficient data transmission function from the security monitoring function, allowing both communication efficiency and security to coexist without direct conflict.
2Productivity
If semantic decoding is used to improve communication efficiency, then communication efficiency is improved, but detection of attacks becomes more difficult
Solution Approach 1:
The patent segments the communication system into distinct functional planes: a data plane for efficient semantic communication and a control plane for security monitoring. This segmentation allows attack detection functions to be separated from the semantic decoding process, making it easier to monitor for attacks without compromising communication efficiency. The D-SMCE entities in the control plane independently monitor for poisoning attacks while the data plane maintains efficient semantic transmission.
Solution Approach 2:
The patent implements feedback mechanisms where D-SMCE entities continuously monitor semantic communications and provide feedback about detected anomalies or attacks. The system uses feedback loops to compare expected semantic interpretations with actual received semantics, enabling automatic detection of poisoning attacks. This feedback approach maintains communication efficiency while improving attack detectability through continuous monitoring and comparison.
Data Source
AI summary
Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving coded data comprising a plurality of data elements, each data element comprising a coded message; determining, for a coded message and based on a semantic model, a semantic distortion value, the semantic model comprising one or more classifiers configured to identify a desired meaning of a given coded message based on a context of the given coded message; determining a difference between the semantic distortion value and a baseline semantic distortion value; and generating, when the difference exceeds a threshold value, a security message indicating a potential security issue.