Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science JOINCS (Journal of Informatics, Network, and Computer Science)
Enas Abbas Abed
University of Diyala
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Comparative study between metaheuristic algorithms for internet of things wireless nodes localization Rana Jassim Mohammed; Enas Abbas Abed; Mostafa Mahmoud El-gayar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp660-668

Abstract

Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Intelligent multimodal identification system based on local feature fusion between iris and finger vein Enas Abbas Abed; Rana Jassim Mohammed; Dhafer Taha Shihab
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp224-232

Abstract

Biometric identification systems, which use physical features to check a person's identity, ensure much higher security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single modal biometric identification system fails to extract enough features for identification. Another disadvantage of using only one feature is not always readable. In this article, a smart multimodal biometric verification model for identifying and verifying a person's identity is recommended based on artificial intelligence methods. The proposed model is identified the iris and finger vein unique patterns each individual to overcome many challenges such as identity fraud, poor image quality, noise, and instability of the surrounding environment. Several experiments were performed on a dataset containing 50 people by using many matching methods. The results of the proposed model were provided a higher accuracy of 98%, with FAR and FRR of 0.0015% and 0.025%, respectively.
A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset: A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset Farah Hatem Khorsheed; Enas Abbas Abed; Zainab Hassan Mohammed; Walaa Badr Khudhair Alwan
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1688

Abstract

The rapid evolution of cyber-attacks—particularly zero-day intrusions and ransomware—has intensified the need for intelligent and resilient detection systems capable of handling imbalanced, high-dimensional network traffic. This research proposes a robust hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced anomaly detection using the UGRansome dataset, a realistic benchmark designed for ransomware and zero-day behavior analysis. The methodology integrates advanced preprocessing, including categorical encoding, feature normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance. The hybrid architecture leverages CNN layers for spatial feature extraction and LSTM layers for modeling temporal dependencies, enabling improved detection of emerging and stealthy threats. Experimental results demonstrate superior performance compared to standalone deep learning baselines, achieving 97.89% accuracy, 0.999 macro AUC, and strong detection capability across minority classes. Confusion matrix visualizations and classification metrics confirm the model’s robustness and generalization. The findings highlight the potential of hybrid deep learning models for proactive cybersecurity defense and establish a foundation for future intelligent intrusion detection systems
Co-Authors Dhafer Taha Shihab Farah Hatem Khorsheed Mostafa Mahmoud El-gayar Rana Jassim Mohammed Walaa Badr Khudhair Alwan Zainab Hassan Mohammed