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
This paper explores the development of interpretable data elements from raw data using Kolmogorov-Arnold Networks(KAN).With the exponential growth of data in contemporary society,there is an urgent need for effective ...This paper explores the development of interpretable data elements from raw data using Kolmogorov-Arnold Networks(KAN).With the exponential growth of data in contemporary society,there is an urgent need for effective data processing methods to unlock the full potential of this resource.The study focuses on the application of KAN in the transportation sector to transform raw traffic data into meaningful data elements.The core of the research is the KANT-GCN model,which synergizes Kolmogorov-Arnold Networks with Temporal Graph Convolutional Networks(T-GCN).This innovative model demonstrates superior performance in predicting traffic speeds,outperforming existing methods in terms of accuracy,reliability,and interpretability.The model was evaluated using real-world datasets from Shenzhen,Los Angeles,and the San Francisco Bay Area,showing significant improvements in different metrics.The paper highlights the potential of KAN-T-GCN to revolutionize data-driven decision-making in traffic management and other sectors,underscoring its ability to handle dynamic updates and maintain data integrity.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。
基金supported by the EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,Grant No.101109045)the National Natural Science Foundation of China(No.NSFC 61925105 and 62171257)the Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute,and the Fundamental Research Funds for the Central Universities,China(No.FRF-NP-20-03).
文摘This paper explores the development of interpretable data elements from raw data using Kolmogorov-Arnold Networks(KAN).With the exponential growth of data in contemporary society,there is an urgent need for effective data processing methods to unlock the full potential of this resource.The study focuses on the application of KAN in the transportation sector to transform raw traffic data into meaningful data elements.The core of the research is the KANT-GCN model,which synergizes Kolmogorov-Arnold Networks with Temporal Graph Convolutional Networks(T-GCN).This innovative model demonstrates superior performance in predicting traffic speeds,outperforming existing methods in terms of accuracy,reliability,and interpretability.The model was evaluated using real-world datasets from Shenzhen,Los Angeles,and the San Francisco Bay Area,showing significant improvements in different metrics.The paper highlights the potential of KAN-T-GCN to revolutionize data-driven decision-making in traffic management and other sectors,underscoring its ability to handle dynamic updates and maintain data integrity.
