We provide an overview of different malware types and the vectors of attacks they leverage for infecting modern vehicles. In this paper, we provide a detailed description of the architecture associated with intelligent vehicles, and identify various security issues and vulnerabilities that impact such systems. Therefore, making them vulnerable to common threats such as malware injection that can compromise the overall security of modern vehicles. This trend is exacerbated by the fact that many of these ECUs rely on wireless communication for interacting with the outside world. Although ECUs are designed to offer various amenities that are associated with modern vehicles including comfort, such features expose new attack surfaces that can be harnessed by attackers. These functionalities are controlled by hundreds of Electronic Control Units (ECUs) which are connected to each other via the Control Area Network (CAN) bus. Recent years have led the path to the evolution of automotive technology and with these new developments, modern vehicles are getting increasingly astute and offering growing quantities of innovative applications that cover various functionalities. The results show that the combination of hardware events as feature inputs, with LSTM and voting network can be effective in detecting new malware. Finally, the voting network is used between the outputs of CNNs and the LSTM network, and the label of the suspicious sample is determined. Then, two Deep Neural Network (DNN) based algorithms, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), are used to construct the machine learning model. In this paper, a novel dynamic malware detection method is presented which utilizes hardware events during file execution such as hardware port status, CPU internal status, main memory status, etc., as features of the classification model. One way to deal with these types of malware is through dynamic malware analysis that requires executing the malware and monitoring its behaviour. Nowadays, malware attempt to deceive all kinds of anti-malware, using obfuscation mechanisms and polymorphic behaviours, thus this method is not able to detect such malware and is not very effective. The most common method for malware detection is a signature-based technique. Hence, malware detection systems are important. So, defence against malware is an important issue in the security of computers and networks.

With the increasing availability of internet access, the number of malware is growing dramatically.

Malware years used runonly to detection