1. A Support Vector Machine (SVM) classifier training device comprising: a computer including at least one microprocessor programmed to train a Support Vector Machine (SVM) one-class classifier using a Radial Basis Function (RBF) kernel K calculated using the equation: 
 K ( x i −x j )= e −γ∥x i −x j )∥ 2 where x ε and γ>0 where is a set of all real numbers, x i and x j are features of a training set, and γ represents a curvature of a hyperplane and varies with message density D in time according to: γ ∝ 1 Var ⁡ ( D ) where Var(D) represents a message density variance of the training set, the SVM one-class classifier being trained by the computer to perform anomaly monitoring of a controller area network (CAN) bus employing a message-based communication protocol by operations including: receiving the training set comprising vectors with associated times representing CAN bus messages; calculating the hyperplane curvature parameter γ functionally dependent on the message density D in time; and training the SVM one-class classifier on the training set using the calculated γ.