proposed ML based algorithm for IoV security system. In addition, [PERSON] et al. [4] has proposed an optimal routing protocol for the alerts generated for the vehicle adhoc network. Smart building systems are easily accessible to attackers as they can be used to connect air-gapped networks to the outside world, allowing external attackers to control and transmit commands to malware installed on a target IoT network. This type of scenario is called HVACKer, which is a codename (that was given by its creators) and is able to interact with a computer's temperature sensor in order to read temperature differences and convert them to zeros and ones (binary code). For example, ForeScout Technologies, which is a cybersecurity company, has found that thousands of insecure IoT devices in HVAC systems are vulnerable to attacks. Around 8000 connected devices, most of them in schools and hospitals, were found allowing illegal access and were highly vulnerable to attacks. HVACKer's manipulation of the HVAC systems allows them to collect private financial data and potentially store illegal data in large corporations. In places like hospitals, faulty smart HVAC systems can pose a serious threat to patients who need to recover at certain temperatures and airflows [2]. In addition, with the advent and advancement ML algorithms, several studies have been conducted, aimed at developing a method that can detect and classify different types of cyber-attacks in general and also targeted at HVAC systems [5]. Most previous studies on HVAC attack detection have achieved the best results using the deep learning model. The main limitation of the DL model is that it requires huge amount of data to train the model and is computationally intensive. In addition to this the nature of the DL models is black box. The internal reasoning of the DL models is hidden. Similarly, some of the ML models such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Ensemble models like Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), etc. also suffered from black box nature. However, the advent of of explainable artificial intelligence (XAI) has alleviated this disadvantage by providing a post-hoc approach to add the explainability to the back box nature models. Recently, XAI has been used in various real-world applications such as healthcare, business, engineering and cybersecurity etc. [6; 7]. However, to the authors knowledge, XAI has not been used in the HVAC system attack detection. Therefore, in the current study, the post-hoc XAI approach was used to make the proposed model transparent. Previous studies focused solely on the security breaches of the BMS network as the detection target and not the HVAC system itself, some of which had used IoT-based security systems and access control strategies in the building devices, authorization, and authentication systems to protect the service access. In contrast, others used a multivariate correlation analysis technique that examines each packet traversing the network for the attacks stored in the database. Nowadays, a large number of studies have been conducted solely focused on how to prevent HVAC cyberattacks, one of these studies was conducted by [PERSON] et al. [8] using the Isolation Forest (IF) technique which is an unsupervised ML algorithm used in the data-driven attack detection strategy along with Convolutional Neural Network (CNN) to encode the feature extraction of temporal data. Isolation Forest works to detect abnormal activity by distinguishing between normal and abnormal. Their model had successfully detected many critical attacks, resulting in a 0.61 recall and 0.90 precision. Furthermore, the main goal of the proposed study is to develop an effective and interpretable detection model that could detect attacks on HVAC systems. The main contribution of the proposed study is articulated as follows:  
* Proposing a proactive model with improved performance to detect attacks on HVAC systems.