Discovering vulnerabilities in network service is of great significance. Currently, coverage-guided fuzzing (CGF) is widely regarded as the most effective method. However, the efficiency of CGF depends on the quality of initial corpus. The initial corpus is a set of valid input examples used to initiate the fuzzing process. Constructing high-quality initial corpus typically requires manual efforts to understand the implementation details and corresponding protocol specifications, making it difficult to generalize across different protocol implementations.To generate high-quality corpus tailored to service under test (SUT), this paper proposes a protocol-independent smart generation method. The paper introduces a novel service communication model and utilizes active learning algorithms to automatically construct the model. By analyzing the minimum spanning tree of the model, we achieve automatic generation of high-quality corpus that adapts to the SUT.We conduct experiment by generating adaptive corpus for 6 targets of 6 different protocols in ProFuzzBench. Compared to the corpus provided by ProFuzzBench, the corpus generated by our system improve the state coverage of modern protocol fuzzers by 37.1% and discover known real protocol vulnerabilities at a speed 2.47x faster.

The aerosol liquid water content (ALWC) dictates the hygroscopicity of aerosol particles. To date, measurements of ALWC have been confined primarily to ground-based observations although vertical profiles of ALWC are crucial for understanding its interactions with meteorology. This study proposes a novel method for deriving profiles of ALWC using data acquired by a Light Detection and Ranging (LiDAR), a microwave radiometer, and a suite of aerosol instruments measuring aerosol physical and chemical properties, deployed during a five-month field experiment in Guangzhou, China. The retrieval approach is based on a machine-learning model named the gradient-boosted decision tree model. The inversion accuracy and stability are assessed through comparisons with ALWC data acquired on the ground and at the top of the Guangzhou tower of 532 m above ground. The agreements are encouraging: with the coefficient of determination$({R} ^{2}) = 0.870$and root-mean-square error (RMSE) =$3.28 ~\mu \text{g} ~\cdot $m−3for all data;${R} ^{2} = 0.776$and RMSE =$2.18 ~\mu \text{g}~ \cdot $m−3for tower data; and${R} ^{2} = 0.872$and RMSE =$4.1~ \mu \text{g} ~\cdot $m−3for ground data. From the vertical distribution of the retrieved ALWC in Guangzhou, ALWC is higher in the lower boundary layer, especially when air pollution is severe. The proportion of liquid water in aerosol particles is closely related to the relative humidity in the environment, which will affect the morphology of aerosol particles (with about every 10% increase in liquid water, the depolarization ratio decreases by 0.02). The model may be of general use for studying air pollution and secondary aerosol generation.