By 2030, the global connected car market size is projected to be USD 361 billion. Automakers such as Mercedes- Benz, BMW, Peugeot, Toyota, and others are investing billions of dollars in software development, autonomous driving, and connected vehicle technology. Many companies are developing strong products to secure connected cars and their environment. However, when a vehicle becomes more connected, it also becomes much more vulnerable to intrusion and cyber-attacks. We investigate the use of classification-based algorithms called supervised machine learning techniques such as Decision Tree, Random Forest, XGBoost, AdaBoost, Support Vector Machines (SVM), Naive Bayes, and K-Nearest Neighbors (KNN) to detect the attacks on vehicles’ identities. We demonstrate, through implementation, the high precision (99%) results obtained with Random Forest, XGBoost, and Support Vector Machines (SVM). However, the execution time for XGboost seems to be faster than the other methods. We evaluate our model against artificial generated data ( using Conditional Tabular GANs ) rather than real data, the accuracy dropped ( the testing accuracy dropped to a mere 56.3%, a 42% decrease ), Confusion matrix reveals widespread misclassification. To enhance accuracy of our model, we proposed two techniques: Deep Transfer Learning (Utilize pre-trained neural network model, the testing accuracy achieved by the model reached 84.67% ) and Ensemble Learning (A machine learning technique combining multiple models for better predictions , they achieved a good accuracy 91% ).