Data flow learning algorithms must be very efficient in learning and predicting sequences. The model that monitors a sequence of data or events can predict the sequel and can act in such a way that it optimally achieves the desired result. Security and digital risk tracking systems are receiving a constant and unlimited input of observations. These data flows are characterized by high variability, as their properties can change drastically and unpredictably over time. Each incoming example can only be processed once, or it must be summarized with a small memory imprint. This research paper proposes the development of an intelligent system, for real-time detection of data flow anomalies related to information systems’ security. Specifically, it describes the implementation of an efficient and high-precision energy-based Online Sequential Extreme Learning Machine (e-b OSELM) that is proposed for the first time in the literature. It is an intelligent model that can detect data dependencies, by applying a measure of compatibility (scalable energy) to each configuration of its variables. It assigns low energy to the correct values and higher energy to the divergent (abnormal) ones. The innovative combination of energy models and ELMs offers high learning speed, ease of execution, minimum human involvement and minimum computational power and resources for anomaly detection and identification.

数据流学习算法在学习和预测序列方面必须非常有效。监控数据或事件序列的模型可以预测后续事件，并以最佳方式实现预期结果。安全和数字风险跟踪系统正在接收持续且无限制的观察输入。这些数据流的特点是高度可变，因为它们的属性会随着时间的推移发生剧烈且不可预测的变化。每个传入的示例只能处理一次，或者必须用一个小的记忆印记来总结。本研究论文提出开发一种智能系统，用于实时检测与信息系统安全相关的数据流异常。具体来说，它描述了在文献中首次提出的高效且高精度的基于能量的在线顺序极限学习机 (eb OSELM) 的实现。它是一种智能模型，可以通过对其变量的每个配置应用兼容性度量（可伸缩能量）来检测数据依赖性。它将低能量分配给正确的值，将较高的能量分配给发散（异常）的值。能量模型和 ELM 的创新组合为异常检测和识别提供了高学习速度、易于执行、最少的人工参与以及最少的计算能力和资源。通过对其变量的每个配置应用兼容性度量（可伸缩能量）。它将低能量分配给正确的值，将较高的能量分配给发散（异常）的值。能量模型和 ELM 的创新组合为异常检测和识别提供了高学习速度、易于执行、最少的人工参与以及最少的计算能力和资源。通过对其变量的每个配置应用兼容性度量（可伸缩能量）。它将低能量分配给正确的值，将较高的能量分配给发散（异常）的值。能量模型和 ELM 的创新组合为异常检测和识别提供了高学习速度、易于执行、最少的人工参与以及最少的计算能力和资源。