# Anomaly Detection for IOT Systems Using Active Learning
## 4 Proposed Methodology
### Active Learning  
Active learning is a machine learning strategy that aims to enhance the accuracy of a model with a reduced number of labeled data points. In this approach, the computer actively selects the specific data points to be marked. Additionally, active learning is a strategy that reduces the expense of annotation and enhances accuracy by choosing the most informative samples for human annotation repetitively, hence requiring fewer labeled examples. Active learning can be employed as a rapid approach to identify and classify atypical data points within a large and intricate dataset to find anomalies in IoT systems. The active learning algorithm selects the most informative data points for labeling by focusing on areas of the dataset where anomalies are anticipated to arise. This approach has the potential to significantly reduce the number of labeled data points needed to achieve the accurate detection of abnormalities. The general architecture of active learning is depicted in Figure 9 above.  
The revised depiction in the experimental environment for the suggested approach, as depicted in Figure 9, offers a thorough representation of the active learning algorithm. The understanding of the query stage is heightened, as it is a crucial element within the active learning process. The classifier is provided with a pool of unlabeled examples during the query step. This stage is of significant importance as it entails the intentional selection of valuable data points from the dataset lacking labels to assign labels to them. By strategically selecting the most valuable samples, we enhance the algorithm's efficiency and efficacy in acquiring knowledge from a restricted set of labelled data. Incorporating the testbed experiment within the image constitutes a noteworthy supplementary inclusion. The testbed experiment constitutes a crucial stage in assessing the performance and efficacy of the algorithm under consideration. The process entails gathering empirical data to evaluate the algorithm's performance inside predetermined parameters or situations. By presenting the testbed experiment in the picture, we underscore the empirical basis of our methodology and its practical relevance. In addition, the revised depiction integrates the procedure of assigning labels to the chosen samples. Labelling the selected samples entails the assignment of accurate and verified labels to the data points that have been specifically selected for annotation. This particular phase is of the utmost importance as it enables us to acquire labelled samples, which can enhance the training data. Including these labelled  
Figure 9: General architecture of active learning.  
samples in the training dataset enhances the precision and dependability of the algorithm's predictions when utilized on the remaining set of unlabeled samples. The revised depiction within the experimental platform for the suggested technique offers a more intricate and all-encompassing portrayal of the active learning algorithm. This statement emphasizes the significance of three key elements: the query stage, the incorporation of a testbed experiment, and the labelling of chosen samples. The comprehension of the active learning process and the efficacy of the proposed algorithm are augmented by the visualization of these crucial steps.