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Fig. 7 SimAttack and the machine learning attack have a similar recall and precision.

Nevertheless, regardless the dataset, SimAttack is slightly better than the machine learning attack.

While SimAttack provides similar performances than the concurrent machine learning attack, SimAttack is much more faster.

Further, in order to measure the vulnerability of proposed PUF, machine learning attack is carried out and the result shows FTL RO based C-CRO PUF is highly resilient to machine learning attack because of its non-linearity property.

We show that SimAttack is faster than the machine learning attack, especially for the logistic regression classifier.

In addition, SimAttack is faster than the machine learning attack especially for a high number of users.

Finally, SimAttack outperforms machine learning attacks and is faster.

More precisely, compared to the previous machine learning attacks, SimAttack divides by 158 and 100 the execution time considering respectively 1,000 users protected by an unlinkability solution and 100 users protected by TrackMeNot.

The performance of the deep model is compared against traditional machine learning approach, and distributed attack detection is evaluated against the centralized detection system.

The normalized alerts are grouped into meta-alerts (fusion, or clustering), which are later classified using machine learning techniques into attacks or false alarms.

The proposed PUF circuit has substantially lesser hardware overhead than previously proposed memristor-based PUF circuits, while being resistant against machine learning based modelling attacks.