基于CB-Attention的JavaScript恶意混淆代码检测方法

JavaScript malicious obfuscation code detection method based on CB-Attention

当今JavaScript代码混淆方法日益多样,现有检测方法在对混淆代检测时会出现漏报和误报的情况,为解决该问题,提出一种基于CB-Attention的JavaScript恶意代码检测方法。由SDPCNN模型和BiLSTM+Attention模型构成,SDPCNN对短距离间的语义特征信息进行提取,BiLSTM+Attention获取JavaScript代码中长距离间的语义信息特征。为验证所提方法的有效性,将该方法与其它方法进行对比,对比结果表明,该方法具有较好的检测效果,F1-Score可达98.78%。

Nowadays,JavaScript code obfuscation methods are becoming more and more diverse,and the existing detection methods show miss and false positives when detecting obfuscation agents.To solve this problem,a JavaScript malicious code detection method based on CB-Attention was proposed.The model was mainly composed of SDPCNN model and BiLSTM+Attention model.SDPCNN was used to extract semantic feature information at short distances,while BiLSTM+Attention was used to obtain semantic information features at long distances in JavaScript code.To verify the effectiveness of the proposed method,the method was compared with other methods.The comparison results show that the method has better detection effects,and the F1-Score can reach 98.78%.