3 citations · 5 across the 3 of their papers we have counts for
6 papers
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples
Rahim Taheri
A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more…
SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment
Marjan Golmaryami, Rahim Taheri, Zahra Pooranian +2
In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities…
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…
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…
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…
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…