用NLP分析恶意软件文本特征，准确率达99.02%。
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
- 将NLP的n-gram技术用于提取恶意软件的字符串和API调用序列特征
- 通过混合特征选择，仅用1.6%原始特征就达到99.02%准确率
- 适合做恶意软件分类的开发者与安全研究人员参考
本文研究了自然语言处理（NLP）中的n-gram分析与机器学习技术在提升恶意软件分类准确性方面的应用。通过n-gram方法从恶意软件样本中提取并分析文本特征，如连续字符串或API调用序列，有效捕捉恶意软件与良性程序之间的独特语言模式，实现更细粒度的分类。研究探讨了n-gram大小选择、特征表示方式及分类算法。在真实世界恶意软件样本上的评估显示，相比传统方法有显著提升。采用混合特征选择技术后，特征集缩减至原始规模的1.6%，在多种机器学习算法下实现99.02%的分类准确率。
原文摘要 · Abstract (English)
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, feature representation, and classification algorithms. While evaluating our proposed method on real-world malware samples, we observe significantly improved accuracy compared to the traditional methods. By implementing our n-gram approach, we achieved an accuracy of 99.02% across various machine learning algorithms by using hybrid feature selection technique to address high dimensionality. Hybrid feature selection technique reduces the feature set to only 1.6% of the original features.
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