# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 3 Taxonomy of Anomaly Detection Techniques
### Anomaly Detection Techniques  
#### 3.1.1 Machine Learning Techniques  
Machine learning anomaly detection techniques for data streams have been proposed in recent years. These techniques can detect data stream anomalies in various core implementations across a broad variety of areas, including manufacturing networks, finance and banking, military, healthcare, insurance, network protection, and the Internet of Things. Figure 2: Taxonomy of anomaly detection techniques. Based on a popular and successful anomaly detection algorithm called Local Outlier Factor (LOF), a new data stream algorithm called Cumulative Local Outlier Factor (C-LOF) was developed; however, it only works on static data [14]. Experimental results suggest that even when detecting anomalies in the data stream, C-LOF may overcome masquerading. A serious drawback of this strategy is that the time complexity of C-LOF is high. An evolving algorithm based on data stream clustering was introduced by [PERSON] et al. [24]. The algorithm called AutoCloud is based on the newly introduced Typicality and Eccentricity Data Analytics principle that is used for anomaly detection research. AutoCloud is an evolving electronic and recursive approach that does not require prior knowledge of data before processing. The algorithm can handle concepts, which are inherited problems in data streams. However, the proposed technique overlooks many of the distance problems, dynamically affecting the separation of the clouds. Another evolving clustering algorithm was proposed based on a mixture of topicalities for data stream mining [25]. It is based on the paradigm of TEDA and separates clustering into two sub-problems: micro and macro-clusters. Experimental results show that the proposed technique yields good results for data clustering and predicts its density even in the face of events that affect data distribution parameters, such as concept drifts. However, the autonomous adaptation process is not entirely considered in this strategy. Complex linear Bayesian equations have been developed, wherein existing BDLMs and RBPF techniques are combined for the real-time detection of anomalies [26]. This methodology aims to quantify if the variables and parameters are effective in detecting anomalies. However, no effort was made in the proposed technique to resolve the challenge of sensor drift or inadequate conditions for implementation. The Hierarchical Temporary Memory (HTM) algorithm is used to suggest a data stream method for real-time outlier detection for space imagers [27]. The HTM-based algorithm is consistent with the detection of spatial and temporal outliers and fulfils the specifications without oversight for real-time, continuous data stream detection. The findings suggest that the algorithm would efficiently achieve real-time detection of anomalies. However, the suggested solution was not able to reduce the false-positive rate. In 2019, an artificial algorithm for the neural network was used by [PERSON] et al. [28]. A novel method for automated anomalies in heterogeneous sensor networks based on data edge exploration combined with cloud data was constructed. The experimental assessment of the planned solution was carried out on real data gathered in an indoor building environment and then distorted by separate virtual impairments. The research outcomes suggest that the proposed solution would adjust itself to the shifts in the environment and identify the anomalies correctly. However, the study does not consider the evolving features of the data and the definition of drift. A new multi-source approach for the detection of multi-dimensional data anomalies (MDADM) was developed [29]. The real-time prediction of the probability of abnormal behavior occurring in underground mining is based on the hierarchical edge computing model. Results show the current method has a higher detection performance and less transmission delay than conventional methods.