activity
20192022
most citedDeep Image: A precious image based deep learning method for online malware detection in IoT Environment

3 citations · 3 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CR20223 cited

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Meysam Ghahramani, Rahim Taheri, Mohammad Shojafar +2

The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Res…

cs.RO2022

Cyrus 2D Simulation Team Description Paper 2016

Nader Zare, Ashkan Keshavarzi, Seyed Ehsan Beheshtian +6

This description includes some explanation about algorithms and also algorithms that are being implemented by Cyrus team members. The objectives of this description are to express…

eess.IV2020

Medical Image Registration Using Deep Neural Networks: A Comprehensive Review

Hamid Reza Boveiri, Raouf Khayami, Reza Javidan +1

Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated i…

cs.CR2019

Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features

Rahim Taheri, Meysam Ghahramani, Reza Javidan +3

In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (A…

cs.LG2019

On Defending Against Label Flipping Attacks on Malware Detection Systems

Rahim Taheri, Reza Javidan, Mohammad Shojafar +3

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of a…

cs.CR2019

Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach

Rahim Taheri, Reza Javidan, Mohammad Shojafar +2

The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solut…