Introducing Crossfire: A New Defense for Graph Neural Networks
What are Graph Neural Networks (GNNs)?
Graph Neural Networks (GNNs) are used in many areas like natural language processing, social networks, and recommendation systems. However, protecting GNNs from attacks is a major challenge.
The Challenge of Bit Flip Attacks (BFAs)
Bit Flip Attacks manipulate bits in a model’s code, weakening its performance and creating security risks. Traditional defenses like honeypots and hashing have limitations—they can detect attacks but often can’t fully restore the network afterward.
Introducing Crossfire
Researchers at the University of Vienna have developed Crossfire, a new solution that enhances existing defenses and effectively restores GNNs after an attack.
How Crossfire Works
- Bit-wise Redundancy Encoding: Crossfire reduces the number of active weights in the GNN, guiding attackers to less critical areas. It uses hashing to monitor weights and honeypots to detect attacks quickly.
- Elastic Weight Rectification: After an attack, Crossfire identifies changes and corrects them at the bit level, ensuring the GNN can recover effectively.
Proven Results
In over 2,160 tests, Crossfire showed a 21.8% better chance of restoring GNNs compared to other methods. It also improved prediction quality by 10.85% on average and can handle up to 55-bit flips from various attacks.
Why Choose Crossfire?
Crossfire is adaptive and efficiently allocates resources based on attack severity, making it a scalable and practical solution for enhancing GNN reliability across various fields.
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