随着网络技术的飞速发展,恶意加密流量已成为网络安全领域的重要威胁。恶意加密流量通过加密技术对恶意数据进行封装,使其难以被传统检测方法识别和拦截。提出一种基于长短期记忆网络(LSTM)和Kolmogorov Arnold Networks(KAN)的恶意加...随着网络技术的飞速发展,恶意加密流量已成为网络安全领域的重要威胁。恶意加密流量通过加密技术对恶意数据进行封装,使其难以被传统检测方法识别和拦截。提出一种基于长短期记忆网络(LSTM)和Kolmogorov Arnold Networks(KAN)的恶意加密流量检测模型——LKAN模型。LSTM能有效捕捉流量数据的时序特征,KAN是一种基于函数分解理论的神经网络,能够高效地学习高维数据的复杂结构,LKAN模型结合LSTM和KAN的优势,进行特征提取和分类,实现了对恶意加密流量的准确识别。利用提出的LKAN模型在ISCX-VPN-NonVPN-2016数据集进行多分类实验,准确率为0.982591,表明了模型的有效性,为恶意加密流量检测方法设计提供了一种新思路。展开更多
U-Net因其简单高效的网络结构,已成为当前医学图像分割领域中的基准模型,在多种图像分割任务中取得了良好的效果。然而,传统U-Net在细节特征提取、跨尺度信息融合以及复杂结构识别方面仍存在一定的局限性,难以充分适应医学图像中存在的...U-Net因其简单高效的网络结构,已成为当前医学图像分割领域中的基准模型,在多种图像分割任务中取得了良好的效果。然而,传统U-Net在细节特征提取、跨尺度信息融合以及复杂结构识别方面仍存在一定的局限性,难以充分适应医学图像中存在的形变、低对比度以及多样性目标等挑战。为进一步提升分割性能,该文提出一种改进模型U-KPD(U-Net with Kolmogorov-Arnold Network and ParNet-Deformable Module)。该模型在U-Net的基础上引入科尔莫哥罗夫-阿诺德网络(Kolmogorov-Arnold Network,KAN),以增强网络对图像局部与全局特征的表达能力,同时结合ParNet-Deformable模块(PD)提升模型对关键区域的自适应建模与形变结构的捕捉能力,从而提高分割的准确性与鲁棒性。通过在CVC-ClinicDB与BUSI两个具有代表性的数据集上开展充分实验验证,结果表明,U-KPD在IoU、Dice系数以及HD95多个评估指标上均优于传统U-Net及其他主流改进模型,尤其在复杂结构、形变目标的识别精度方面表现更为优异,具有良好的通用性与应用前景。展开更多
In this study,we used the Kolmogorov-Arnold networks(KAN)model based on the Kolmogorov-Arnold representation theorem for a comprehensive and fair evaluation.We compare its performance with four other powerful classifi...In this study,we used the Kolmogorov-Arnold networks(KAN)model based on the Kolmogorov-Arnold representation theorem for a comprehensive and fair evaluation.We compare its performance with four other powerful classification models across three datasets:a simple slope binary classification dataset,an imbalanced rockburst dataset,and a highly discrete liquefaction dataset.First,a thorough review of machine-learning algorithms for geohazard assessment was conducted.Subsequently,three datasets were collected from real engineering practices,and their data structures were visualized.Bayesian optimization was then used to adjust the parameters of all models across all datasets.To ensure model interpretability,a global sensitivity analysis based on Sobol indices was performed,establishing an interpretable visual analysis of the model's decision-making process.For a fair evaluation,various metrics and repeated stratified 10-fold cross-validation were employed to comprehensively analyze the predictive results of the models.The results indicate that although the KAN model,based on the RBF kernel,achieves the expected performance on the binary classification dataset,it also performs well on imbalanced and highly discrete datasets,significantly surpassing other commonly used classification models.This demonstrated the broad application potential of the KAN model in geotechnical engineering.展开更多
文摘随着网络技术的飞速发展,恶意加密流量已成为网络安全领域的重要威胁。恶意加密流量通过加密技术对恶意数据进行封装,使其难以被传统检测方法识别和拦截。提出一种基于长短期记忆网络(LSTM)和Kolmogorov Arnold Networks(KAN)的恶意加密流量检测模型——LKAN模型。LSTM能有效捕捉流量数据的时序特征,KAN是一种基于函数分解理论的神经网络,能够高效地学习高维数据的复杂结构,LKAN模型结合LSTM和KAN的优势,进行特征提取和分类,实现了对恶意加密流量的准确识别。利用提出的LKAN模型在ISCX-VPN-NonVPN-2016数据集进行多分类实验,准确率为0.982591,表明了模型的有效性,为恶意加密流量检测方法设计提供了一种新思路。
文摘U-Net因其简单高效的网络结构,已成为当前医学图像分割领域中的基准模型,在多种图像分割任务中取得了良好的效果。然而,传统U-Net在细节特征提取、跨尺度信息融合以及复杂结构识别方面仍存在一定的局限性,难以充分适应医学图像中存在的形变、低对比度以及多样性目标等挑战。为进一步提升分割性能,该文提出一种改进模型U-KPD(U-Net with Kolmogorov-Arnold Network and ParNet-Deformable Module)。该模型在U-Net的基础上引入科尔莫哥罗夫-阿诺德网络(Kolmogorov-Arnold Network,KAN),以增强网络对图像局部与全局特征的表达能力,同时结合ParNet-Deformable模块(PD)提升模型对关键区域的自适应建模与形变结构的捕捉能力,从而提高分割的准确性与鲁棒性。通过在CVC-ClinicDB与BUSI两个具有代表性的数据集上开展充分实验验证,结果表明,U-KPD在IoU、Dice系数以及HD95多个评估指标上均优于传统U-Net及其他主流改进模型,尤其在复杂结构、形变目标的识别精度方面表现更为优异,具有良好的通用性与应用前景。
基金supported by the National Natural Science Foundation of China(Grant Nos.42107214 and 42477157).
文摘In this study,we used the Kolmogorov-Arnold networks(KAN)model based on the Kolmogorov-Arnold representation theorem for a comprehensive and fair evaluation.We compare its performance with four other powerful classification models across three datasets:a simple slope binary classification dataset,an imbalanced rockburst dataset,and a highly discrete liquefaction dataset.First,a thorough review of machine-learning algorithms for geohazard assessment was conducted.Subsequently,three datasets were collected from real engineering practices,and their data structures were visualized.Bayesian optimization was then used to adjust the parameters of all models across all datasets.To ensure model interpretability,a global sensitivity analysis based on Sobol indices was performed,establishing an interpretable visual analysis of the model's decision-making process.For a fair evaluation,various metrics and repeated stratified 10-fold cross-validation were employed to comprehensively analyze the predictive results of the models.The results indicate that although the KAN model,based on the RBF kernel,achieves the expected performance on the binary classification dataset,it also performs well on imbalanced and highly discrete datasets,significantly surpassing other commonly used classification models.This demonstrated the broad application potential of the KAN model in geotechnical engineering.
