基于混合嵌入和胶囊网络的恶意URL分类模型

Malicious URL Classification Model Based on Hybrid Embedding and Capsule Network

作为最常见的网络安全威胁之一,恶意URL攻击每年都会造成巨大的经济损失,尽管研究人员已经提出了许多方法来检测恶意URL,但现有方法存在不能充分利用URL提供的有用信息和对URL中的区分性特征提取不充分的问题,导致分类性能不佳。提出了一种基于混合嵌入和胶囊网络的恶意URL分类模型,引入高速网络和胶囊网络从URL的混合嵌入表示中提取区分性特征,提高了模型的分类性能。在公开数据集ISCX-URL2016上的实验结果表明,该文模型与其他基线方法相比,实现了更好的分类性能,能够有效应对混淆技术的多样性。

As one of the most frequent cybersecurity threats,malicious URLs cause huge financial loss every year.Although many malicious URL detection methods have been proposed,current methods cannot make full use of the useful information provided by URL as well as extract enough discriminative features from URL,resulting in poor classification performance.This paper proposes a malicious URL classification model based on hybrid embedding and capsule network.The model applies highway network and capsule network to extract discriminative features from the hybrid embeddings of URLs,which improves the classification performance.The experimental results on the ISCX-URL2016 dataset prove that the proposed model can achieve better classification performance than other baselines,and can effectively deal with the diversity of obfuscation techniques.