Prof. Iman AlMomani
Prof. Iman AlMomani
Full Professor, Senior IEEE Member, Top 2% Scientists (Sandford), University of Jordan
Verified email at - Homepage
Cited by
Cited by
WSN-DS: A dataset for intrusion detection systems in wireless sensor networks
I Almomani, B Al-Kasasbeh, M Al-Akhras
Journal of Sensors 2016, 2016
Ubiquitous GPS vehicle tracking and management system
IM Almomani, NY Alkhalil, EM Ahmad, RM Jodeh
2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing …, 2011
Enhancing outpatient clinics management software by reducing patients’ waiting time
I Almomani, A Alsarheed
Journal of infection and public health 9 (6), 734-743, 2016
Digitization of healthcare sector: A study on privacy and security concerns
M Paul, L Maglaras, MA Ferrag, I AlMomani
ICT Express, 2023
Ransomware detection system for Android applications
S Alsoghyer, I Almomani
Electronics 8 (8), 868, 2019
A multi-layer classification approach for intrusion detection in iot networks based on deep learning
R Qaddoura, A M. Al-Zoubi, H Faris, I Almomani
Sensors 21 (9), 2987, 2021
Android ransomware detection based on a hybrid evolutionary approach in the context of highly imbalanced data
I Almomani, R Qaddoura, M Habib, S Alsoghyer, A Al Khayer, I Aljarah, ...
IEEE Access 9, 57674-57691, 2021
A multi-stage classification approach for iot intrusion detection based on clustering with oversampling
R Qaddoura, AM Al-Zoubi, I Almomani, H Faris
Applied Sciences 11 (7), 3022, 2021
A comprehensive analysis of the android permissions system
IM Almomani, A Al Khayer
Ieee access 8, 216671-216688, 2020
Optical bit-plane-based 3D-JST cryptography algorithm with cascaded 2D-FrFT encryption for efficient and secure HEVC communication
W El-Shafai, IM Almomani, A Alkhayer
IEEE Access 9, 35004-35026, 2021
Non-intrusive speech quality prediction in VoIP networks using a neural network approach
M Al-Akhras, H Zedan, R John, I Almomani
Neurocomputing 72 (10-12), 2595-2608, 2009
On the effectiveness of application permissions for Android ransomware detection
S Alsoghyer, I Almomani
2020 6th conference on data science and machine learning applications (CDMA …, 2020
Visualized malware multi-classification framework using fine-tuned CNN-based transfer learning models
W El-Shafai, I Almomani, A AlKhayer
Applied Sciences 11 (14), 6446, 2021
An automated vision-based deep learning model for efficient detection of android malware attacks
I Almomani, A Alkhayer, W El-Shafai
IEEE Access 10, 2700-2720, 2022
A federated learning framework for cyberattack detection in vehicular sensor networks
M Driss, I Almomani, Z e Huma, J Ahmad
Complex & Intelligent Systems 8 (5), 4221-4235, 2022
Optimizing extreme learning machines using chains of salps for efficient Android ransomware detection
H Faris, M Habib, I Almomani, M Eshtay, I Aljarah
Applied Sciences 10 (11), 3706, 2020
IoT botnet detection using salp swarm and ant lion hybrid optimization model
R Abu Khurma, I Almomani, I Aljarah
Symmetry 13 (8), 1377, 2021
FEAR: Fuzzy-based energy aware routing protocol for wireless sensor networks
IM ALMomani, MK Saadeh
International Journal of Communications, Network and System Sciences 4 (06), 403, 2011
A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks
SB Atitallah, M Driss, I Almomani
Sensors 22 (11), 4302, 2022
Efficient Denial of Service Attacks Detection in Wireless Sensor Networks
I Almomani, M Alenezi
Journal of Information Science and Engineering 34 (4), 977-1000, 2018
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