Meet the Crossfire: a wide protection frame of graph neural networks under flip attacks

The networks of graph nials (GNNS) receive applications in different cases, such as processing environmental, environmental analysis, recommendations, etc. Due to its widespread use, improves GNNS defenses have come up as a critical challenge. As he explored the endangered methods of attacks, researchers received flip attacks (BFA). In the same situation, BFA is developed for NEURAL Neal Neal Networks (CNNs), but recent development has shown that this increases in GNNS. Current ways to protect GNNS with sensitive limits; They will not be able to restore network completely after attacks or requires an expensive attack. Therefore, the University of Vienna Researchers have developed a novel solution, crossing the fire, which can effectively use existing methods for protecting and returning networks.

Bit-flipping attacks to deceive each bits within a double-learning binary code. This weakens the model performance, creating a great risk of safety. Honeypots and protection based on heat is current defense method. Honeypot protects work by including several high items within the program; Any alternative or many elements can indicate attacks. The invaders, however, now passes these metals. Hashing-based protection is used by a strong Cryptographic bath to leave changes to weight. They can't, however, prepare the damage caused.

The proposed model, the Crossfire, is a model, the hybrid receiving bass with honeypot and a protection based on heat and returns model after a small mass attack. The key is – the method of engaging fire by:

• Bit-Wise Redundancy Encoding: Crossfire sets some instruments in zero to reduce the number of active instruments in GNN. This guides attackers in critical months, preventing severe injury. The bath regularly monitors active instruments, to find any changes. Honeypot weight is well placed in attracting attackers and quickly point to attack.
• Elastic Weight Redification: The first layer of Hashes points to the conversion after attack, then line and column hashmes directly. Adjustment is done using HoneyPot at a small level or zerod if some options fail.

By all 2,160 trials, the fire showed the highest level of 21,8% of the rebuilding GNN in its pre-strike attack. The framework has developed a multi-remedial process for 10.85% on average. Crossfire maintained a higher performance that reaches 55 flips from a variety of attacks. In addition, the Framepwork's Adapwork is allowing the resources based on the attacks found, making it a well-effective and powerful solution.

In conclusion, robbery crosses improves GNN protection capacity in a new Bit-Flip attack, which works well and works very well. The strongest of the Crosshire Reaculity is carefully considable for attacks attacks, guarantees strong safety and prominent performance and put a new rate of accessing the gnns in contradictions. Because it is useful and operating, it provides a promising method to improve the reliability of GNN programs in all many fields.

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