In today’s automotive market, the OEM’s bring out more and more new connected features in their cars to make the users driving experience more safe and amusing than ever, but this comes with a very big price: the attackers gain even more attack surface thanks to the broad connectivity. One of the biggest problem is the approach of the manufacturers to this subject, most of them are not really implementing the much needed security solutions and testing the system’s cyber resilience unless they are forced to do it by the law or regulations. But to be able to defend themselves against the attacks, first they have to detect it. To address this problem, we developed a hybrid solution that uses multiple machine learning models to leverage the classification capabilities of these algorithms and to combine this with a human-modifiable IDS ruleset to effectively detect various attacks, while providing explainability and auditablity.