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首页> 外文期刊>ACM transactions on intelligent systems >A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis
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A Multi-Label Multi-View Learning Framework for In-App Service Usage Analysis

机译：应用内服务使用情况分析的多标签多视图学习框架

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摘要

The service usage analysis, aiming at identifying customers' messaging behaviors based on encrypted App traffic flows, has become a challenging and emergent task for service providers. Prior literature usually starts from segmenting a traffic sequence into single-usage subsequences, and then classify the subsequences into different usage types. However, they could suffer from inaccurate traffic segmentations and mixed-usage subsequences. To address this challenge, we exploit a multi-label multi-view learning strategy and develop an enhanced framework for in-App usage analytics. Specifically, we first devise an enhanced traffic segmentation method to reduce mixed-usage subsequences. Besides, we develop a multi-label multi-view logistic classification method, which comprises two alignments. The first alignment is to make use of the classification consistency between packet-length view and time-delay view of traffic subsequences and improve classification accuracy. The second alignment is to combine the classification of single-usage subsequence and the post-classification of mixed-usage subsequences into a unified multi-label logistic classification problem. Finally, we present extensive experiments with real-world datasets to demonstrate the effectiveness of our approach. We find that the proposed multi-label multi-view framework can help overcome the pain of mixed-usage subsequences and can be generalized to latent activity analysis in sequential data, beyond in-App usage analytics.
机译：服务使用情况分析旨在基于加密的App通信流来识别客户的消息传递行为，已成为服务提供商一项具有挑战性的紧急任务。现有文献通常从将业务量序列划分为单次使用子序列开始，然后将这些子序列分类为不同的使用类型。但是，它们可能会遭受不正确的流量分割和混合用途子序列的困扰。为了应对这一挑战，我们采用了多标签多视图学习策略，并为应用内使用情况分析开发了增强的框架。具体来说，我们首先设计出一种增强的流量分割方法，以减少混合使用子序列。此外，我们开发了一种多标签多视图逻辑分类方法，该方法包括两个路线。第一种对齐方式是利用流量子序列的包长视图与时延视图之间的分类一致性，提高分类精度。第二个比对是将单一用途子序列的分类和混合用途子序列的后分类合并为一个统一的多标签逻辑分类问题。最后，我们使用现实世界的数据集进行了广泛的实验，以证明我们方法的有效性。我们发现，提出的多标签多视图框架可以帮助克服混合使用子序列的痛苦，并且可以将其推广到顺序数据中的潜在活动分析，而不仅仅是应用内使用情况分析。

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