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首页> 外文期刊>Journal of Computer Virology and Hacking Techniques >Building a practical and reliable classifier for malware detection
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Building a practical and reliable classifier for malware detection

机译：构建实用且可靠的分类器以检测恶意软件

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摘要

Having a machine learning algorithm that can correctly classify malicious software has become a necessity as old methods of detection based on hashes and hand written heuristics tend to fail when dealing with the intensive flow of new malware. However, in order to be practical, the machine learning classifiers must also have a reasonable training time and a very small amount, preferably zero, of false positives. There were a few authors who addressed both these issues in their papers but creating such a model is more difficult when more than 3 million files are involvedeeded in the training. We mapped a zero false positive perceptron in a new space, applied a feature selection algorithm and used the resulted model in an ensemble, voting or a rule based clustering system we’ve managed to achieve a detection rate around 99 % and 0.07 % false positives while keeping the training time suitable for large data sets.
机译：拥有能够正确分类恶意软件的机器学习算法已成为一种必要，因为在处理大量新恶意软件时，基于哈希和手写启发式的旧检测方法往往会失败。然而，为了实用，机器学习分类器还必须具有合理的训练时间和非常少的假阳性，最好为零。有一些作者在论文中同时解决了这两个问题，但是当培训中涉及/需要超过300万个文件时，创建这样的模型会更加困难。我们在新空间中映射了一个零误报感知器，应用了特征选择算法，并将结果模型用于基于集合，投票或基于规则的聚类系统中，我们设法实现了99％和0.07％的误报率同时使训练时间适合于大数据集。

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