# Investigation of Smart Machines with DNAs in SpiderNet
## 1 Introduction  
Advancements in technology have reached a stage where devices such as drones, robots, and smart cars equipped with AI infrastructure are more accessible than ever before. The Internet of Things (IoT) and artificial intelligence (AI) are well-established concepts, and research has increasingly combined the two to enhance the efficiency of IoT systems, resulting in what is now termed AIoT. Drones, robots, and vehicles play a pivotal role in these developments and are employed across various domains, including personal use, academia, business, government, intelligence, and even criminal activities. As these advanced technologies become more deeply integrated into everyday life, digital forensics has had to adapt its methods to include forensic investigations of robots, vehicles, and drones. Digital forensics, broadly defined, is \"a process that focuses on recovering, retrieving, and analysing material and data stored or transmitted digitally, in order to reconstruct past events related to one or more cases\". However, much like mobile applications, digital forensics is not a one-size-fits-all field; different technologies require tailored tools and approaches to ensure effective investigations. Robot forensics focuses on the investigation of cyber-physical systems, which are often imagined as futuristic, human-like machines but, in reality, are intricate systemsintegrating hardware, software, mechanical components, sensors, and communication networks. According to [1], robots interact with their physical environments through these components and are widely used in industries and services to automate tasks and augment human activities. In robot forensics, investigators analyse control systems, sensor data, and communication logs to understand robot behaviour, particularly within the IoT ecosystem [2]. Vehicle forensics has evolved in tandem with the growing connectivity of modern cars to technologies such as Bluetooth, GPS, and autonomous driving systems [3]. These systems, along with third-party applications and services, generate vast amounts of data crucial for traffic investigations or criminal cases [4]. Such data allow investigators to link vehicles to specific users and reconstruct the events leading to an incident. The Controller Area Network (CAN) infrastructure and its protocols facilitate internal communication logs within vehicles, which can provide forensically valuable data about the vehicle [5]. Drone forensics, by contrast, focuses on analysing flight logs, onboard sensors, and communication systems. Drones, or Unmanned Aerial Vehicles (UAVs), store essential data such as GPS information, communication logs, and media recordings, which can serve as valuable digital evidence [6]. As [7] highlights, drone investigations require examining both hardware and software components, including firmware and communication systems. With the increasing use of drones in unlawful activities such as smuggling and unauthorized surveillance, drone forensics has become an essential aspect of modern investigative work. A significant challenge in robot, vehicle, and drone forensics lies in the proprietary nature of the systems developed by manufacturers. Unique protocols and encryption methods often hinder forensic investigations. Addressing this challenge requires the standardisation of forensic methodologies across these domains to ensure consistency and reliability in investigative processes. Additionally, the rapid pace of technological advancement demands continuous updates to forensic techniques to keep up with emerging technologies. Future advancements in these fields include developing more effective identification systems for devices to enhance investigative efficiency. Despite the significant technological advancements, there remains a lack of research into preventative measures and investigation methods specifically designed for these devices.