Seed scheduling is a critical step of greybox fuzzing, which assigns different weights to seed test cases during seed selection, and significantly impacts the efficiency of fuzzing. Existing seed scheduling strategies rely on manually designed models to estimate the potentials of seeds and determine their weights, which fails to capture the rich information of a seed and its execution and thus the estimation of seeds’ potentials is not optimal. In this article, we introduce a new seed scheduling solution, Graphuzz, for coverage-guided greybox fuzzing, which utilizes deep learning models to estimate the potentials of seeds and works in a data-driven way. Specifically, we propose an extended control flow graph called e-CFG to represent the control-flow and data-flow features of a seed's execution, which is suitable for graph neural networks (GNN) to process and estimate seeds’ potential. We evaluate each seed's code coverage increment and use it as the label to train the GNN model. Further, we propose a self-attention mechanism to enhance the GNN model so that it can capture overlooked features. We have implemented a prototype of Graphuzz based on the baseline fuzzer AFLplusplus. The evaluation results show that our model can estimate the potential of seeds and has the robust capability to generalize to different targets. Furthermore, the evaluation using 12 benchmarks from FuzzBench shows that Graphuzz outperforms AFLplusplus and the state-of-the-art seed scheduling solution K-Scheduler and other coverage-guided fuzzers in terms of code coverage, and the evaluation using 8 benchmarks from Magma shows that Graphuzz outperforms the baseline fuzzer AFLplusplus and SOTA solutions in terms of bug detection.

Federated Learning (FL) has recently drawn considerable attention, enabling multiple end devices to collaboratively learn global models without collecting device data. In reality, end devices can usually be distributed in non-correlated environments and generate non-IID data, which may lead to the Artificial Intelligence (AI) model weight divergence among devices and model accuracy degradation after aggregation. In this paper, to address the non-IID data problem for FL, we treat this problem among end devices as a distribution adaptation problem among multiple source domains and analyze the feasibility of feature augmentation, and then propose a novel method called Label-wisE Distribution Adaptive Federated Learning (LEDA-FL). First, to reduce the divergence in the label-wise feature space, we integrate the modified Conditional Variational AutoEncoder (CVAE) to align the label-wise feature distributions among clients. Second, we augment the label-wise features for FL clients to improve the FL performance (test accuracy and communication efficiency). Finally, we conduct an extensive experiment on five popular datasets, and the experimental results show that our proposed method improves the test accuracy of the global model (e.g.,6.2% test accuracy improvement on CIFAR100 compared to FedProx) and the communication efficiency of FL (e.g., about 60% reduction in communication cost on CIFAR100 compared to FedProx).