Q
Qasem Abu Al-Haija
Researcher at Tennessee State University
Publications - 92
Citations - 744
Qasem Abu Al-Haija is an academic researcher from Tennessee State University. The author has contributed to research in topics: Computer science & Intrusion detection system. The author has an hindex of 6, co-authored 46 publications receiving 147 citations. Previous affiliations of Qasem Abu Al-Haija include King Faisal University & Petra University.
Papers
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Proceedings ArticleDOI
Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network
TL;DR: This work proposes an accurate and inclusive computational breast cancer diagnosis framework using ResNet-50 convolutional neural network to classify histopathological microscopy images into benign or malignant images.
Journal ArticleDOI
An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks
TL;DR: The comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network).
Journal ArticleDOI
Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
TL;DR: This paper develops, investigates, and evaluates the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks, and demonstrates that bagging ensemble techniques (BAG-DT) offer better accuracy and lower error rates than other implemented supervised learning techniques.
Journal ArticleDOI
IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method
Khalid Albulayhi,Qasem Abu Al-Haija,Suliman A. Alsuhibany,Ananth Abhishek Jillepalli,Mohammad Ashrafuzzaman,Frederick T. Sheldon +5 more
TL;DR: A novel feature selection and extraction approach for anomaly-based IDS that is superior and competent with a very high 99.98% classification accuracy is proposed and compared with other state-of-the-art studies.
Journal ArticleDOI
ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks
TL;DR: An ensemble learning model for botnet attack detection in IoT networks called ELBA-IoT that profiles behavior features of IoT networks and uses ensemble learning to identify anomalous network traffic from compromised IoT devices is proposed.