R
Raouf Khayami
Researcher at Shiraz University of Technology
Publications - 21
Citations - 1353
Raouf Khayami is an academic researcher from Shiraz University of Technology. The author has contributed to research in topics: Deep learning & Malware. The author has an hindex of 10, co-authored 19 publications receiving 834 citations. Previous affiliations of Raouf Khayami include Islamic Azad University & Shiraz University.
Papers
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A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
TL;DR: A novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks is presented.
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A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting
Hamed HaddadPajouh,Ali Dehghantanha,Raouf Khayami,Kim-Kwang Raymond Choo,Kim-Kwang Raymond Choo +4 more
TL;DR: The potential of using Recurrent Neural Network (RNN) deep learning in detecting IoT malware by using RNN to analyze ARM-based IoT applications’ execution operation codes (OpCodes) is explored.
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An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications
TL;DR: A high-performance approach based on the Max–Min Ant System (MMAS), which is an efficient variation in the family of ant colony optimization algorithms, is proposed to tackle the static task-graph scheduling in homogeneous multiprocessor environments, the predominant technology used as mini-servers in fog computing.
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Know Abnormal, Find Evil: Frequent Pattern Mining for Ransomware Threat Hunting and Intelligence
TL;DR: In this article, the authors used sequential pattern mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms.
Journal ArticleDOI
DRTHIS: Deep ransomware threat hunting and intelligence system at the fog layer
Sajad Homayoun,Ali Dehghantanha,Marzieh Ahmadzadeh,Sattar Hashemi,Raouf Khayami,Kim-Kwang Raymond Choo,David Ellis Newton +6 more
TL;DR: The Deep Ransomware Threat Hunting and Intelligence System (DRTHIS), a deep learning system to distinguish ransomware from goodware and identify their families, uses Long Short-Term Memory and Convolutional Neural Network, two deep learning techniques, for classification using the softmax algorithm.