随机LU分解：一种字典构造算法
Randomized LU decomposition: An Algorithm for Dictionaries Construction
摘要：
近年来，独特的字典建设已经获得了重要性，因为他在数据处理的有用性。通常，从训练数据构建一个或多个字典，然后使用它们来对未参与训练过程的信号进行分类。介绍了一种新的字典构造算法。它是基于一个低秩矩阵因式分解正在实现的随机LU分解的应用程序的训练数据。这种方法是快速的，可扩展的，可并行化的，消耗低内存，优于SVD在这些类别中，也非常好地工作在大型稀疏矩阵。与现有方法相比，随机LU分解构造了一个欠完备字典，简化了新到达信号的构造和分类过程。字典结构是通用的和通用的，适合不同的应用。我们证明了该算法的文件类型识别，这是一个基本的任务，在数字安全领域，现在执行例如沙箱机制，深度包检测，防火墙和防病毒系统的能力。我们提出了一种基于内容的方法，检测既不依赖于文件扩展名，也不依赖于元数据的文件类型。这种方法是很难欺骗，我们表明，只有几个文件片段，从一个完整的文件需要一个成功的分类。基于构造的字典，我们表明，该方法可以有效地识别PDF文件中的执行代码片段。$\textbf{Keywords.}$字典构建，分类，LU分解，随机LU分解，基于内容的文件检测，计算机安全。
英文摘要：
In recent years, distinctive-dictionary construction has gained importance due to his usefulness in data processing. Usually, one or more dictionaries are constructed from a training data and then they are used to classify signals that did not participate in the training process. A new dictionary construction algorithm is introduced. It is based on a low-rank matrix factorization being achieved by the application of the randomized LU decomposition to a training data. This method is fast, scalable, parallelizable, consumes low memory, outperforms SVD in these categories and works also extremely well on large sparse matrices. In contrast to existing methods, the randomized LU decomposition constructs an under-complete dictionary, which simplifies both the construction and the classification processes of newly arrived signals. The dictionary construction is generic and general that fits different applications. We demonstrate the capabilities of this algorithm for file type identification, which is a fundamental task in digital security arena, performed nowadays for example by sandboxing mechanism, deep packet inspection, firewalls and anti-virus systems. We propose a content-based method that detects file types that neither depend on file extension nor on metadata. Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification. Based on the constructed dictionaries, we show that the proposed method can effectively identify execution code fragments in PDF files. $\textbf{Keywords.}$ Dictionary construction, classification, LU decomposition, randomized LU decomposition, content-based file detection, computer security.
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