## 2 Related Work
### Secure ACN Systems  
Primary studies have investigated various aspects of general automotive systems, communication protocols, data encryption methods, and vulnerability assessments. Threat assessment is not limited to the network but also to the vehicle itself [8]: some given examples of authenticity attacks are the Sybil attack, falsified entity attack, replication attack, Global Navigation Satellite System (GNSS) spoofing and injection attack, and timing attack. The system investigated in [10] shows the existence of multiple components within a simple ACN system: a secure-access server that requires login credentials, and a mobile application running on the terminals. The server processes the gathered data and notifies the authorities with designated emergency contacts, while the application interacts with the user. [PERSON] et al. [11] highlights the usage of event data recorder (EDR) for cybersecurity event reconstruction. As a general overview, the study provides an examination of the sensors integrated within emergency call (eCall) systems, including crash detection sensors, positional and velocity data systems, and communication methodologies. Additionally, the paper conducts a comparative analysis of existing eCall device solutions, considering factors such as their degree of autonomy, technical implementation, and the range of services they offer. A method for testing the security of vehicles based on threat modeling was proposed in [12]. In particular, it is performed manually by cybersecurity experts, and the attack trees are created in a specific format. The penetration tests developed by the authors were based on the attack trees, with the primary goal of assessing the security of the system. These tests were designed to comprehensively check the vulnerabilities and potential weaknesses within the system. As part of this evaluation, each attack scenario was assigned both a privacy severity rating and an operational severity rating. These ratings served as quantitative measures to assess the potential impact and consequences of the identified attacks on both the privacy of the system's users and the operational integrity of the system itself. The authors of [13] introduced a highly effective security system tailored for mobile vehicles through the use of an short message service (SMS) alert system. This system stands out due to its integration of a microcontroller, setting it apart from other comparable systems. The components employed in this proposed approach are intricately linked with accident detection, storage of contact numbers, and SMS transmission. The safeguarding of vehicles revolves around the identification of accidents, executed by a vibration sensor. The notification of this detection is conveyed through SMS alerts to mobile devices via Global System for Mobile Communications (GSM). The enclosed embedded system, comprising these components, finds its place within the vehicle, serving as an accident detection mechanism. A study in this domain was conducted by the authors of [14]. They designed a system to analyze audio streams to detect road accidents. The underlying hypothesis posits that sound can be deconstructed into atomic audio units, much like words in a text. The appearance of specific audio units, termed \"audio words\", within a given time frame serves as a distinguishing factor for identifying specific sounds. Injury risk assessment is one of the major risks when considering the ACN system; an extensive analysis was conducted in [15], the studies goal was to assess the severity of crashes by utilizing information collected before the crash occurred. Additionally, the study aimed to uncover the underlying mechanism of impact response (IR) using appropriate interpretation techniques. The impulse-momentum theory was employed to introduce innovative mathematical formulations for several indicators of crash severity. These indicators encompassed metrics such as a change in velocity (delta V), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI).