Sentence examples for machine learning attacks from inspiring English sources

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.

Results show that while the recall provided by SimAttack is lower than the recall provided by the machine learning attack with both classifiers, SimAttack outperforms the machine learning attacks in term of precision (87.1 % versus 29.8 % and 77.8 % for the logistic regression and the SVM, respectively).

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.

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

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

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

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

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

We now compare the performance of both SimAttack and the concurrent machine learning attack on unlinkability solutions.

We now compare the performance of both SimAttack and the concurrent machine learning attack on TrackMeNot using the TMN100 dataset.