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As shown in Table V, the most contributing features in descending order were ID (ID of the 10 frames), dt(time difference
between timestamps of 10 frames), dt_ID(the time difference between each individual CAN ID of 10 frames), win_most_freq_id (number of occurrences of the most frequent CAN ID per window), win_dur (time duration from first frame (t set to 0) to the last frame in the window), win_mean_dt (mean time difference between the CAN-frames in the sliding window). The same order of importance was also observed by the LIME feature values. Among the control models, the models are affected by the selected feature set which also affects their individual performances. For VGRF to give its best performance, the most contributing control model in both the SAD and ORNL dataset was RF max and its dynamic weights were the highest. RF min was the least contributing control model and its dynamic weight was the least which however helped VGRF achieve its highest performance.
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IEEE/ICACT20220035 Slide.11
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