S
Sadaqat Ur Rehman
Researcher at Namal College
Publications - 62
Citations - 866
Sadaqat Ur Rehman is an academic researcher from Namal College. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 12, co-authored 51 publications receiving 333 citations. Previous affiliations of Sadaqat Ur Rehman include Beijing University of Technology & Edinburgh Napier University.
Papers
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Journal ArticleDOI
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.
Andrew Churcher,Rehmat Ullah,Jawad Ahmad,Sadaqat Ur Rehman,Fawad Masood,Mandar Gogate,Fehaid Alqahtani,Boubakr Nour,William J Buchanan +8 more
TL;DR: In this paper, the authors compared several machine learning (ML) methods such as k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) for both binary and multi-class classification on Bot-IoT dataset.
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A Lightweight Chaos-Based Medical Image Encryption Scheme Using Random Shuffling and XOR Operations
Fawad Masood,Maha Driss,Wadii Boulila,Jawad Ahmad,Sadaqat Ur Rehman,Sana Ullah Jan,Abdullah Qayyum,William J. Buchanan +7 more
TL;DR: The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.
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A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.
TL;DR: In this paper, a one-dimensional CNN with two convolutional layers, two down-sampling layers, and a fully connected layer was proposed for electrocardiogram (ECG) classification.
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Security in Fog Computing: A Novel Technique to Tackle an Impersonation Attack
Shanshan Tu,Muhammad Waqas,Sadaqat Ur Rehman,Muhammad Aamir,Obaid Ur Rehman,Zhang Jianbiao,Chin-Chen Chang +6 more
TL;DR: This work investigates PLS that exploits the properties of channel between end user and fog node to detect the impersonation attack in fog computing network and proposes Q-learning algorithm to attain the optimum value of test threshold in the impersonated attack.
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Reinforcement Learning Assisted Impersonation Attack Detection in Device-to-Device Communications
Shanshan Tu,Muhammad Waqas,Sadaqat Ur Rehman,Talha Mir,Ghulam Abbas,Ziaul Haq Abbas,Zahid Halim,Iftekhar Ahmad +7 more
TL;DR: In this article, a reinforcement learning-based technique was proposed to guarantee identification of the impersonator based on channel gains in device-to-device (D2D) communications, where the channel gain between a transmitter and a receiver is difficult to predict due to channel variations.