基于TCN与ChebyKAN融合网络的恶意软件多分类模型OA

The traditional malware detection methods based on API(application programming interface)call sequences fail to sufficiently capture the long-term temporal dependencies and neglect the high-order nonlinear relationships among features.To address these issues,this paper proposes a multi-classification model for malware(TCN-SE-ChebyKAN)that integrates a temporal convolutional network(TCN)and Chebyshev-Kolmogorov-Arnold network(ChebyKAN).First,the TCN module is employed to extract features from API call sequences.By leveraging causal convolutions and dilated convolutions,the model captures temporal characteristics and long-range dependencies representing malware behavior,thereby obtaining more comprehensive behavioral information.Next,a squeeze-and-excitation(SE)network module is introduced to construct a channel attention mechanism.Through dynamic adjustment of channel weights,the model enhances its ability to capture discriminative features.Finally,the KAN(Kolmogorov-Arnold network)module is utilized to model complex relationships among features.By improving the KAN module with Chebyshev polynomials,the model strengthens its capability to model high-order nonlinear relationships among features,boosting overall detection performance.Experimental results demonstrate that the proposed model achieves an AUC value of 92.53%on the Mal-API-2019 data set,with significant improvements in other detection metrics.