This paper presents a logistic regression based approach to predict the soft-response for a challenge using the total delay-difference as an input. This approach enables us to determine whether a challenge is stable or not. Soft-response is the probability of response bit corresponding to the challenge being 1. The total delay-difference is computed from the input challenge by assuming that the delay-difference of the stages are known. The approach learns a logistic function based on the total delay-difference which has just 3 parameters. Therefore, this is a simple approach which gives comparable performance against a more complex approach based on artificial neural network (ANN) models. The model demonstrates good sensitivity and precision but poor specificity. Furthermore, we use scaling parameter of the logistic function to study its relation to the arbiter's timing parameters like setup and hold time.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Apr 26 2018|
|Event||2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy|
Duration: May 27 2018 → May 30 2018
|Name||Proceedings - IEEE International Symposium on Circuits and Systems|
|Other||2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018|
|Period||5/27/18 → 5/30/18|
Bibliographical noteFunding Information:
This research has been supported by the National Science Foundation under grant number CNS-1441639 and the semiconductor research corporation under contract number 2014-TS-2560.
- MUX PUF
- logistic regression
- physical unclonable function
- stable challenges