## 2 Related Works
When working with real-world times series, the data collection is thought to be easy; the annotation of data, however, is time-consuming and relatively expensive. The works surveyed in [38] demonstrate a solution to this problem through deep transfer learning. In essence, deep transfer learning allows for the training of a model on a far more extensive or diverse dataset, even if it is unrelated to the current problem. This pre-trained model gains far more general features from the larger datasets, which can be transferred effectively to the target problem during the fine-tuning stage on the much smaller and more limited dataset. Another concern stemming from the lack of data in cybersecurity is the performance creep of many models on these well-known benchmark datasets. Part of the survey performed in this work is to illustrate that, even in recent years, studies are still being conducted on decades-old and obsolete datasets, likely due to the lack of available data. With datasets so over-explored, researchers may feel the need to set themselves, attempting \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Publication** & **Accuracy** \\ \hline KDD CUP 99 [10; 11] & [13] & 97.5\% (IVQ), 99.4\% (RBF), 99.86\% (MLP) \\ \hline  & [12] & 93\% (SVM), 94.4\% (K-NN), 96.6\% (SNORT-NB) \\ \hline NSL-KDD [14] & [16] & 80.1\% (CVAE) \\ \hline UNSW15-NB15 [19] & [22] & 99\% (LSTM, GRU), 99.95\% (FastGRNN) \\ \hline  & [20] & 86.68\% (PSO-LightGBM \& OC-SVM) \\ \hline ToN-IoT [28] & [28] & 88\% (Binary Class CART), 77\% (Multi-Class CART) \\ \hline IoT-23 [31] & [33] & 99\% (Binary and Multi-Class 1D, 2D and 3D CNNs) \\ \hline  & \multicolumn{3}{c}{**Detection Rate (DR) and False Alarm Rate (FAR)**} \\ \hline  & \multicolumn{3}{c}{80.95\% DR (Anomaly Based MapReduce), 96.20\% DR (Misuse Based MapReduce), 1.44\% FAR} \\ NSL-KDD [14] & [17] & MapReduce), 5.92\% FAR (Anomaly Based MapReduce), 1.44\% FAR \\  & \multicolumn{3}{c}{(Misuse Based MapReduce)} \\ \hline  & \multicolumn{3}{c}{**True Positive Rate (TPR) False Positive Rate (FPR)**} \\ \hline N-BaloT [23] & [23] & 100\% (TPR Deep-AE), 0.007\% (TPR Deep-AE) \\ \hline  & \multicolumn{3}{c}{**F1-Score**} \\ \hline N-BaloT [23] & [27] & 0.996\%-0.9998 (Deep-AE) \\ \hline  & \multicolumn{3}{c}{**Lost Feature Reconstruction Accuracy**} \\ \hline NSL-KDD [14] & [16] & 99.02\% (Protocol CVAE), 92.84\% (Flag CVAE), 71.89\% (Services CVAE) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the reviewed works. techniques that may end up negating the validity of their model, such as the biased selection of samples for training and testing as a subset of an overall dataset. Far too many models are achieving results of 99% and 100% on datasets. These results are unlikely to transfer to real-life scenarios and should strongly indicate that either the model or dataset is lacking (either with models trained on the path of least resistance features, or datasets far too simple for the ever-adapting cyber landscape). There is a clear need for more datasets as well as datasets of varying data types. This need guides the research that follows in this study. Dragon_Pi and Dragon_Slice represent initial steps to explore the effectiveness of power consumption data in security. The insights gained during this study will guide future efforts to incorporate heterogeneous data sources into the security pipeline.