1 code implementation • 5 Mar 2024 • Ehsan Nowroozi, Imran Haider, Rahim Taheri, Mauro Conti
In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data.
no code implementations • 18 Apr 2022 • Rahim Taheri
Then, by examining the methods for computing the uncertainty, the defense method is proposed to increase the accuracy to about 70%.
no code implementations • 16 Apr 2022 • Marjan Golmaryami, Rahim Taheri, Zahra Pooranian, Mohammad Shojafar, Pei Xiao
In the SETTI architecture, we design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and ASelf-MDS.
no code implementations • 4 Apr 2022 • Meysam Ghahramani, Rahim Taheri, Mohammad Shojafar, Reza Javidan, Shaohua Wan
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
no code implementations • 13 Aug 2019 • Rahim Taheri, Meysam Ghahramani, Reza Javidan, Mohammad Shojafar, Zahra Pooranian, Mauro Conti
We test our experiments in a different type of features: API, intent, and permission features on these three datasets.
no code implementations • 13 Aug 2019 • Rahim Taheri, Reza Javidan, Mohammad Shojafar, Zahra Pooranian, Ali Miri, Mauro Conti
Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.
no code implementations • 20 Apr 2019 • Rahim Taheri, Reza Javidan, Mohammad Shojafar, Vinod P, Mauro Conti
We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix.