Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

中文翻译：

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使用基于 HTM 的语义折叠技术的异常行为检测框架

根据人类新皮层的工作原理，已经开发了分层时间记忆模型，这是一个提出的序列学习理论框架。HTM 处理分类和数字类型的数据。语义折叠理论 (SFT) 基于 HTM 以稀疏分布式表示 (SDR) 的形式表示要处理的数据流。对于自然语言感知和生产，SFT 为语言学习阶段的语义基础基础提供了语义证据描述的坚实结构背景。异常是来自不遵循预期行为的数据流的模式。任何数据模式流都可能具有多种异常类型。在数据流中，偏离和偏离标准、正常或预期的单一模式或密切相关模式的组合称为静态（空间）异常。时间异常是模式之间的一组意外变化。当首次出现变化时，这将被记录为异常。如果此更改多次出现，则将其设置为“新常态”并作为异常终止。HTM 系统检测异常，并且由于持续学习的性质，当它们成为新常态时，它会快速学习。使用基于 HTM 的 SFT 来改进决策的稳健异常行为检测框架（SDR-ABDF/P2）是本研究中提出的框架或模型。研究人员声称，所提出的模型将能够通过使用无监督学习规则在时间序列中连续学习几个变量的顺序。