Author
Contributions by role
Author 3
Fida Muhammad Khan
Department of Computer Science, Qurtuba University of Science and Information Technology, 25000, Peshawar, Pakistan
Summary
Fida Muhammad Khan is currently pursuing a Ph.D. in Computer Science at Qurtuba University of Science and Information Technology, Peshawar, Pakistan. He did his MS in Computer Science at the University of Science and Technology, Bannu, Pakistan. His research interests include Data Mining, Cybersecurity, IoT, Machine Learning, Deep Learning, and Natural Language Processing (NLP).
Edited Journals
IECE Contributions

Open Access | Research Article | 31 March 2025
Enhancing Authentication Security in Internet of Vehicles: A Blockchain-Driven Approach for Trustworthy Communication
IECE Transactions on Advanced Computing and Systems | Volume 1, Issue 1: 48-62, 2025 | DOI: 10.62762/TACS.2025.835144
Abstract
The Internet of Vehicles (IoVs) is an emerging technology that enhances transportation systems by enabling interactions between vehicles, infrastructure, and other entities. Securing IoV networks from cyber threats like eavesdropping, data tampering, and intrusions is a major challenge. This research presents a Blockchain-Enabled Secure Authentication Protocol for IoVs (BESA-IOV), which leverages blockchain’s decentralized and tamper-resistant nature for secure communication in vehicular networks. By utilizing ECC-based lightweight cryptography and blockchain-based public key management, it ensures strong authentication, confidentiality, and integrity. The results show that BESA-IOV signif... More >

Graphical Abstract
Enhancing Authentication Security in Internet of Vehicles: A Blockchain-Driven Approach for Trustworthy Communication

Open Access | Research Article | 29 March 2025
ViTDroid and Hybrid Models for Effective Android and IoT Malware Detection
IECE Transactions on Advanced Computing and Systems | Volume 1, Issue 1: 32-47, 2025 | DOI: 10.62762/TACS.2024.521915
Abstract
This paper introduces ViTDroid, a novel hybrid model that combines Vision Transformers (ViTs) and recurrent neural networks (RNNs) to enhance Android and IoT malware detection. ViTDroid addresses critical challenges by leveraging ViTs to capture global spatial dependencies and RNNs (LSTM and GRU) to model temporal patterns, enabling comprehensive analysis of complex malware behaviors. Additionally, the model integrates explainability tools, such as LIME and SHAP, to enhance transparency and trustworthiness, essential for real-world cybersecurity applications. The study evaluates ViTDroid's performance against conventional models, including RNN, LSTM, and GRU, using accuracy, precision, recal... More >

Graphical Abstract
ViTDroid and Hybrid Models for Effective Android and IoT Malware Detection

Free Access | Research Article | 31 December 2024
Vehicular Network Security Through Optimized Deep Learning Model with Feature Selection Techniques
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 136-153, 2024 | DOI: 10.62762/TSCC.2024.626147
Abstract
In recent years, vehicular ad hoc networks (VANETs) have faced growing security concerns, particularly from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks flood the network with malicious traffic, disrupting services and compromising resource availability. While various techniques have been proposed to address these threats, this study presents an optimized framework leveraging advanced deep-learning models for improved detection accuracy. The proposed Intrusion Detection System (IDS) employs Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN) alongside robust feature selection techniques, Random Projecti... More >

Graphical Abstract
Vehicular Network Security Through Optimized Deep Learning Model with Feature Selection Techniques