Published Paper Details:

As more and more autonomous and smart vehicles are used in ground transportation systems, new security problems arise. This change from operations run by people to those run by computers makes it easier for bad people to attack. As the Internet of Things (IoT) becomes more common in cars, they are always making and sharing a lot of data. Attackers can take advantage of this trend's weaknesses in complicated ways, like with Advanced Persistent Threats (APT). It is very important to be able to find APTs in vehicles that have IoT technology. To find these threats, we need better ways to do so. The urgent requirement for vehicle data privacy constrains conventional centralised Machine Learning (ML) methodologies. Also, there aren't many APT datasets available to the public in the vehicle field, which makes it harder to develop and test models. This is a big problem for cybersecurity in this area that is always changing. This study introduces an innovative Federated Deep Neural Network (FDNN) framework incorporating a privacy-preserving technique to address these challenges. The research emphasises the primary obstacles in APT detection and delineates its distinctive contributions to the domain. It talks about the research questions that are guiding the study. The UNSW-NB15, Edge-IIoTset, and CSE-CIC-IDS2018 datasets each represent a different stage of an APT attack. We use these datasets to look at and judge the framework that was made. For these datasets, the framework without the privacy-preserving technique gets APT detection accuracies of 97.32%, 96.81%, and 98.06%, respectively. But when the privacy-preserving technique is used, the framework's accuracies are 95.62%, 96.11%, and 95.63%, respectively. There are tables that show all of the results, as well as other evaluation metrics like Precision, False Positive Rate, and F1 Score. We use "Shapley Additive Explanations (SHAP)" analysis on the framework we made to find the most important features for finding APTs. This study validates the efficacy of a novel framework for identifying APTs in distributed vehicular contexts. The framework works well because it cuts down on the amount of data and the number of features. This was shown by extensive testing with several benchmark datasets. Future work will look into how well the framework can find APTs in different areas.