# Anomaly Detection for IOT Systems Using Active Learning
## 2 Literature Review
Their work trained ML classifiers using the UNSW-NB15 dataset. The study tested Naive Bayes (NB), LR, KNN, and RF to detect intrusion. Classifier accuracy, precision, recall, F1-score, Mean Squared Error (MSE), False Positive Rate (FPR), and True Positive Rate (TPR) were tested with and without feature selection procedures. These machine learning classifiers were also compared and according to the findings, the RF algorithm uses all of the information that is accessible to it to obtain an accuracy of 99.5%; when only certain features are used, the accuracy rises to 99.6%. To build accurate IDSs, [22] developed the XGBoost method as a form of feature selection in conjunction with several ML approaches, including DT, LR, ANN, KNN, and SVM. To compare methods, the researchers used the UNSW-NB15 dataset. The experimental results demonstrate that by adopting the XGBoost-based feature selection strategy, methods like DT may increase their test accuracy in binary classification from 88.13% to 90.85%. Similarly, [24] used features from the UNSW-NB15 dataset to discover sets of features based on flow, Message Queuing Telemetry Transport (MQTT), and Transmission Control Protocol (TCP). Overfitting, the curse of dimensionality, and dataset imbalances were all eliminated. To train the clusters, they used supervised machine learning techniques like ANN, SVM, and RF. The authors' RF-based binary classification accuracy was 98.67%, while their multi-class classification accuracy was 97.37%. Using RF on Flow and MQTT features, TCP characteristics, and the best features from both clusters, they achieved classification accuracies of 96.74%, 91.96%, and 96.56%, respectively, using cluster-based approaches. They also show that the suggested feature clusters outperform other state-of-the-art supervised ML algorithms in terms of accuracy and training time. The implementation of distributed deep learning approaches for the detection of IoT threats has highlighted recent developments in the field of IoT security ([PERSON] et al., 2020) [25]. In addition, artificial intelligence has become a crucial tool for spotting irregularities in energy use in buildings, according to a thorough analysis of the latest developments and prospects by [PERSON] et al. [26]. A hierarchical hybrid intrusion detection system developed specifically for IoT applications was also suggested by [PERSON] et al., demonstrating the expanding variety of IoT security measures [27]. The use of semi-supervised hierarchical stacking temporal convolutional networks has also shown promise in anomaly detection for IoT connectivity, as [PERSON] et al. have shown [28]. Additionally, [PERSON] et al. created a two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks, highlighting the significance of strong security measures in the IoT ecosystem [29]. This literature review section has examined and evaluated different methodologies that have been proposed to tackle anomaly detection in IoT systems. Furthermore, traditional approaches are frequently needed to help in accommodating IoT environments' ever-changing and dynamic nature, where anomalies may exhibit varying appearances over time. In summary, this literature analysis has underscored the importance of the various methodologies employed in identifying anomalies within IoT systems. Our proposed active learning methods can potentially improve the accuracy and efficiency of anomaly detection by eliminating the need for labeling and allowing for flexibility in dynamic IoT environments. The primary objective of this research article is to make a valuable contribution to the existing pool of knowledge in this particular field. Doing so will establish a solid groundwork for potential future developments in the domain of IoT anomaly detection, explicitly focusing on the utilization of active learning techniques. Table 1 presents a comprehensive compilation of previous references, encompassing datasets, parameters, techniques, and corresponding outcomes.