随着信息化的发展和网络应用的增多,许多软硬件产品受到各种类型的网络安全漏洞影响.漏洞分析和管理工作往往需要对大量漏洞情报文本进行人工分类.为了高效准确地判断漏洞情报文本所描述漏洞的类别,提出了一种基于多层双向Transformer...随着信息化的发展和网络应用的增多,许多软硬件产品受到各种类型的网络安全漏洞影响.漏洞分析和管理工作往往需要对大量漏洞情报文本进行人工分类.为了高效准确地判断漏洞情报文本所描述漏洞的类别,提出了一种基于多层双向Transformer编码器表示(bidirectional encoder representation from Transformers,BERT)的网络安全漏洞分类模型.首先,构建漏洞分类数据集,用预训练模型对漏洞情报文本进行特征向量表示.然后,将所得的特征向量通过分类器完成分类.最后,使用测试集对分类效果进行评估.实验共使用了48000个包含漏洞描述的漏洞情报文本,分别用TextCNN,TextRNN,TextRNN_Att,fastText和所提模型进行分类.实验结果表明,所提模型在测试集上的分类评价指标得分均为最高,能够有效应用于网络安全漏洞分类任务,降低人工工作量.展开更多
In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language proc...In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.展开更多
文摘随着信息化的发展和网络应用的增多,许多软硬件产品受到各种类型的网络安全漏洞影响.漏洞分析和管理工作往往需要对大量漏洞情报文本进行人工分类.为了高效准确地判断漏洞情报文本所描述漏洞的类别,提出了一种基于多层双向Transformer编码器表示(bidirectional encoder representation from Transformers,BERT)的网络安全漏洞分类模型.首先,构建漏洞分类数据集,用预训练模型对漏洞情报文本进行特征向量表示.然后,将所得的特征向量通过分类器完成分类.最后,使用测试集对分类效果进行评估.实验共使用了48000个包含漏洞描述的漏洞情报文本,分别用TextCNN,TextRNN,TextRNN_Att,fastText和所提模型进行分类.实验结果表明,所提模型在测试集上的分类评价指标得分均为最高,能够有效应用于网络安全漏洞分类任务,降低人工工作量.
基金Project(60763001)supported by the National Natural Science Foundation of ChinaProjects(2009GZS0027,2010GZS0072)supported by the Natural Science Foundation of Jiangxi Province,China
文摘In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.
