## 2 Review of Literature
[8], presents a HVAC system model consisting of 12 zones created with the Transient System Simulation Tool (TRNSYS), which was utilized to analyze the cybersecurity of HVAC systems. The dataset was divided into two logs containing regular operational data logs with a total of 194,301 samples in the first log and 8840 in the second log. The data was collected and used to build and test a semi-supervised data-driven attack detection technique namely Isolation Forest (IF). The proposed technique is compared to traditional ML algorithms and showed promising results in attack detection with high reliability and minimal computational cost, achieving a precision of 0.90 and a recall of around 0.61. In summary, the studies to date have used different datasets and methods to detect attacks and anomalous traces and visualized their results. However, few studies have applied ML algorithms and there is a need for further improvements and investigations. Table 1 contains the summary of the previous related studies. Finally, in our study, we aim to propose an interpretable model for the proactive detection of attacks specifically related to HVAC systems. Therefore, in the current study we will use the HVAC dataset published by [PERSON] et al. [8]. Based on the author's knowledge there is only one study that used the current dataset. One of the limitations of the research [8] was that the proposed solution could only identify attacks that adversely affect system function by changing the temperature comfort levels and/or generating extreme energy consumption. Also, the study only represented the performance of its proposed algorithms using only used two evaluation measure namely, precision and recall. In the current study, we will apply and evaluate several ML algorithms. Furthermore, we aimed to propose a model with the enhanced performance compared to the benchmark study. We attempt to develop an accurate and interpretable model using ML and XAI. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Technique** & **Dataset** & **No of Features** & **Results** \\ \hline
[9] & 2021 & Fusion-based method & 15 speech commands & 136 & Accuracy = 0.941 \\ \hline
[10] & 2017 & Unsupervised anomaly detection & real data collected over 2.5 years period & - & Precision = 0.91 \\ \hline
[11] & 2020 & RadViz visualization algorithm & VAST Challenge 2016: Mini-Challenge dataset & - & - \\ \hline
[12] & 2021 & BMM, Correntropy & IoTBot-IDS & 13 & Accuracy = 0.992 \\ \hline
[13] & 2021 & Functional Shape Analysis (FSA) & IoT (SWaT) system & - & - \\ \hline
[14] & 2020 & XGBost & NSL-KDD & 8 &
\begin{tabular}{c} Accuracy = 0.97 \\ Precision = 0.97 \\ Recall = 0.968 \\ F-Score = 0.968 \\ \end{tabular} \\ \hline
[15] & 2021 & KNN & UNSW-NB15 & 9 &
\begin{tabular}{c} Accuracy = 0.99 \\ Precision = 0.9432 \\ Recall = 0.998 \\ F-score = 0.97 \\ \end{tabular} \\ \hline
[16] & 2021 & Dynamic Feature Selector & NSL-KDD, UNSW-NB15 & 124 &
\begin{tabular}{c} Accuracy = 0.9965 \\ (NSL-KDD) \\ Accuracy = 0.9276 \\ (UNSW-NB15) \\ \end{tabular} \\ \hline
[17] & 2021 & Single Wavelet Entropy (WSE) & - & - & Accuracy = 0.965 \\ \hline
[18] & 2018 & Wavelet transform and DNN & Synthetic simulated power system dynamics & - & Accuracy > 0.90 \\ \hline
[19] & 2021 & CDBN & FDIA dataset & - & Accuracy = 0.984 \\ \hline
[20] & 2021 & EMD-MFCC with multi kernel SVM & Three datasets 960, 1152, and 12624 sentences & 12 & Accuracy > 0.90 \\ \hline
[8] & 2020 & IF &
\begin{tabular}{c} HVAC system \\ Log1: 194301 \\ Log2: 8840 \\ \end{tabular} & Log1: 51 & Precision = 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the previous related studies.