A Survey on Time Series Data Classification: Blockchain Technologies and Security Concerns

Abstract

The difficulties about user security and privacy have appeared as significant concerns in recent years. The number of cyber-attacks grows at a concerning velocity, hence rendering internet users susceptible to malicious activities perpetrated by hackers. Data mining approaches are employed to extract accurate results from massive and complex databases. Furthermore, the utilization of Blockchain (BC) approaches is increasingly popular in current Internet of Things (IoT) applications as an opportunity to address issues related to privacy and security. Lots of studies have been performed on algorithms for data mining and techniques concerning blockchain. Time series data is a commonly used form of data. Time Series Classification (TSC) refers to the creation of predictive models that generate a target variable or label based on linear or sequential data inputs across a considerable duration. The possible results may be presented in either ordinal or numerical form. Even so, previous studies have shown major limitations when it comes to handling privacy and security issues that can’t be applicable in dynamic instances, as well as the substantial computational cost necessary. Moreover, correctly determining the amount of sensitive parameters required to complete the classification process remains a challenge. We have put forth a comprehensive survey on the classification of blockchain data. In the first phase of our study, we conducted an analysis and categorization of both conventional data classification approaches and contemporary time series data classification techniques. We further discussed limitations and strengths of existing techniques. Finally, we highlight future research problems and directions.