# 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
However, the authors overlooked the evolution of data and its value and that its characteristics may change over time, which will affect the overall accuracy of the proposed method for anomaly detection. A new approach to classifying non-stationary data streams that address problems in detecting anomalies such as infinite time, idea drift, recurring concepts, and concept evolution was suggested based on a multi-kernel approach [30]. These kernels were modified in the stream periodically after acquiring the precise labels of the instances. In the function spaces, newly arrived cases will be graded as to their distance from the boundaries of the groups previously identified. The experimental results indicate the proposed approach is superior in this area to the state-of-the-art approaches. However, the study does not consider the evolving clusters. For the first time, an outlier detection algorithm called xStream, which addresses feature-evolution in data streams, was suggested by [PERSON] et al. [31]. The proposedalgorithm addresses a problem that has not been investigated previously. XStream is an outlier detector based on density and has three main characteristics: (1) it is a constant-space and constant-time algorithm (per incoming update), (2) it measures anomalies on several scales or granularities, and (3) it can handle high-dimensionality by distance-preserving projections, and non-stationary as the stream progresses through (1)-time model updates. For evolving streams with moderate space overhead for which there is no competition, experiments show that xStream is effective and precise. However, the challenge of evolving the cluster, which affects the overall outlier detection, has not been attempted. A regression-based strategy was developed by [PERSON] et al. [32] for detecting contextual anomalies in the control system of air traffic. They also have actionable improvement specifics by using the algorithm for modification detection and time windows on contextual anomalies. The evaluation of the proposed model shows a high accuracy anomaly detection with low delay. However, the researchers did not discuss the challenge of fault detection initiatives. An SVM algorithm was effectively designed by [PERSON] et al. [33], to provide drivers with real-time warnings and directions on the road anomalies and ensure a safe driving experience. The proposed system uses the common machine learning technique, SVM, to categorize driving events, such as acceleration and braking, and road anomalies, such as bumps and potholes, and it gives drivers real-time alerts and guidance using a local Quick Dynamic Time Warping (FastDTW) algorithm. Nevertheless, the study fails to consider the different categories and features of objects moving on the roads. In 2018, HTM was used by [PERSON] et al. [34] to detect outliers on real-time network metrics obtained by continuous analysis of the resource utilization of workflow tasks performed. The framework can process data streams online in an unsupervised fashion and successfully adapt to changes in the data based on the underlying statistics. The experiment results illustrate the proposed model's ability to correctly catch output deviations on different resource usage parameters caused by various conflicting workloads implemented into the program. However, a severe weakness with this argument is that detection rate accuracy and latency are still subject to improvement. Similarly, HTM has been used to create a new method of detecting anomalies based on online sequence memory [35]. Spatial and temporal anomalies within predictable and noisy domains can be detected by the proposed method. The technique achieves, without supervision, real-time, continuous, online detection criteria. The results indicate an improvement in the detection rate of the method in comparison with the state of the art. However, the research did not consider evaluating the technique based on a real dataset with anomalies containing high dimensionality.