# Anomaly Detection for IOT Systems Using Active Learning
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. Dataset/Parameters** & **Methodology** & **Limitations** & **Results** \\ \hline - & Datasets like UNSW-NB15 and & & & - & A relatively small sample size & \\ - & In this way, 71,537,674 Denial of & & & was used for evaluating the & \\  & Service (DoS) incidents yield & & & proposed active learning & \\  & 25,000 random samples. From 75 & & & approach for wireless IoT & - \\  & Comma-Separated Values (CSV) & & & intrusion detection. & \\  & files containing 1,821,639 & & & - & Does not provide information on & \\  & information-gathering incidents, & & & the number of instances or the & \\  & 25,000 are randomly extracted, & & & diversity of the dataset used. & \\  & and 1587 of those are theft & & & - & Lacks of evidence of a & \\  & occurrences. In total, 6430 are & & & real-world implementation of & \\  & retrieved from 75 files containing & & & the proposed approach & \\  & 9543 normal instances. & & & \\ \hline - & The study evolves around two & & & \\  & datasets, UNSW-NB15 and & & & - & Limited dataset for evaluating & \\  & CICID52017. & & & the effectiveness of ensemble & - \\  & - & The first dataset consists of & - & techniques used in & techniques for anomaly & \\
[10] & - & A total of 25,070 samples were & & this model include & detection. & - \\  & used for testing and 13,961 & & RE, KNN, ANN, & - & Absence of a comparative & 70.4\%, RF is 91\%, \\  & samples for training. & & and SVM. & analysis with other existing & & and 79.5\% for ANN. \\  & - & The second dataset has 2,720,634 & & methods for intrusion detection. & \\  & records in its sample space and & & & & \\  & 77 features. & & & & \\ \hline - & To investigate the proposed & - & Machine learning & - & Lacks practical implementation & - \\  & VLSTM model, the UNSW-NB15 & & & and validation in real-world & - & For VLSTM, the \\  & dataset is used. & & NB, RF, AdaBoost, & - & Does not provide a & \\  & - & Approximately 42 features are & & Neural Network & \\  & included in the dataset. & & (CNN) are used. & & experiments comparison with & \\  & & & & \\  & & & & \\ \hline - & The authors have employed the & & - & The research paper lacks details & \\  & UNSW-NB15 dataset. & & & on testing scenarios, & \\  & There are 45 network attributes & & & comparisons with existing & \\  & in the dataset overall, including & & & anomaly detection techniques in & \\  & and flow. Other classifications & & & fog computing architectures, & \\  & include flow, fundamentals, & & & metrics for the custom lab & \\  & substance, time, and other & & via Machine & - & RF accuracy of 96\%, \\  & & & Learning models & & DIT accuracy of \\  & substance, time, and other & & & hike RF, LR, KNN, & \\  & & & DT, and 1D-CNN. & - & The evaluation metrics utilized & \\ - & With 175,341 training and 82,331 & & & in the research paper to assess & \\  & testing records in CSV format, & & the performance of the custom & \\  & the dataset has almost 2.5 & & & tab transformer for anomaly & \\  & million records in total. & & & detection in fog computing & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of past references, including datasets/parameters, methodology, limitations, and results.