生物启发的数据采掘：将恶意软件签名视为生物序列
Bio-inspired data mining: Treating malware signatures as biosequences
摘要：
机器学习在生物信息学问题上的应用已经很成熟。生物信息学技术在机器学习中的应用，特别是将非生物数据表示为生物序列，还不太为人所知。本文的目的是探索氨基酸表示有问题的机器学习数据的影响，并评估生物信息学工具和技术补充传统机器学习的好处。将60种计算机病毒和60种计算机蠕虫的特征转化为氨基酸表示，并首先分别进行多重比对，以确定每个类别（病毒和蠕虫）中不同家族的保守区域。随后对所有120个比对的标签进行第二次比对，以便在输入到许多机器学习技术之前鉴定非保守区域。第一次比对后病毒和蠕虫签名之间的长度差异通过第二次比对解决。我们的第一组实验表明，将计算机恶意软件签名表示为氨基酸序列，然后进行比对，可以提高分类和预测的准确性。我们的第二组实验表明，检查人工病毒和蠕虫数据的数据采掘的结果与已知的蛋白质可以导致从自然发生的蛋白质的域的恶意软件签名的概括。然而，需要进一步的工作来确定不同的表示和序列比对方法处理有问题的机器学习数据的优点和缺点。
英文摘要：
The application of machine learning to bioinformatics problems is well established. Less well understood is the application of bioinformatics techniques to machine learning and, in particular, the representation of non-biological data as biosequences. The aim of this paper is to explore the effects of giving amino acid representation to problematic machine learning data and to evaluate the benefits of supplementing traditional machine learning with bioinformatics tools and techniques. The signatures of 60 computer viruses and 60 computer worms were converted into amino acid representations and first multiply aligned separately to identify conserved regions across different families within each class (virus and worm). This was followed by a second alignment of all 120 aligned signatures together so that non-conserved regions were identified prior to input to a number of machine learning techniques. Differences in length between virus and worm signatures after the first alignment were resolved by the second alignment. Our first set of experiments indicates that representing computer malware signatures as amino acid sequences followed by alignment leads to greater classification and prediction accuracy. Our second set of experiments indicates that checking the results of data mining from artificial virus and worm data against known proteins can lead to generalizations being made from the domain of naturally occurring proteins to malware signatures. However, further work is needed to determine the advantages and disadvantages of different representations and sequence alignment methods for handling problematic machine learning data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
