Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Hamid Masood Khan,Aurangzeb Khan,Fazal Masud Khan,Daniyal M Alghazzawi,Muhammad Zubair Asghar,Waqas Haider Bangyal

doi:10.1155/2021/5585238

Abstract

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.

Highlights

Nowadays, anomalous behavior detection in streaming applications is a challenging task
Keeping in view the Hierarchical Temporal Memory (HTM) model, the main research problem is formulated as follows: How can we develop a robust framework that can detect anomalous behavior from real-time data streams and convert them into simultaneous prediction vectors based on computed threshold value for comparison using HTM based semantic folding?
A robust anomalous behavior detection framework using HTM based on Semantic Folding Theory (SFT) for improving decision-making (SDR-ABDF/P2) is required, which is what we address in this study

Summary

Introduction

Anomalous behavior detection in streaming applications is a challenging task. The system must process data and output a decision in real time for a quick decision, rather than making many passes or batches of files. In a number of cases of real-world scenarios, the sample of sensor streams is huge enough having a little opportunity for a let alone expert’s intervention. The real goal emphasis is prevention, rather than detection, so it is vitally desired and required to detect anomalous behavior as early as possible, giving enough actionable information ideally well before any chance of system failure. It is a difficult task to detect anomalous behavior and compare it with any existing standard. In addition to this, real-time applications impose their own specific requirements and challenges that must be considered before taking decisions on results

Methods

Results

Conclusion