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All Journal International Journal of Electrical and Computer Engineering
Ikram, Muhammad Jawad
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Computer Science & IT Electrical & Electronics Engineering

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Federated public key infrastructure management for secure internet of things interoperability Abdulkader, Omar Ahmed; Ikram, Muhammad Jawad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp792-802

Abstract

Proliferating the internet of things (IoT) across all industry fields offers numerous possibilities for invention. It also multiplied the issues of ensuring uniform interoperability among a wide range of devices and platforms. The focus of this paper is to propose an approach for enhancing the security of IoT networks. This study investigated the possible efficacy of employing a federal public key infrastructure (PKI) structure system to serve IoT-based ecosystems. To achieve this goal, we have developed an elaborate experimental framework incorporating different trust models, security protocols, privacy enhancements, and performance metrics that demonstrate the practical benefits of this kind of federated system. One of the contributions of this study is an experimental model that mimics a real IoT ecosystem. It entails many IoT devices, installing vital PKI elements, and the development of safe information transmission channels. Measurements such as latency pointed out the feasibility of various IoT concepts, including such short response time as 2.8 ms for vehicular IoT (V2X). Measures for interoperability ranged with V2X having a 96.4% success, indicating the strength of the standards within that segment. This study reveals the benefits of a federated PKI management system for solving issues of IoT interconnectedness.
Exploring topic modelling: a comparative analysis of traditional and transformer-based approaches with emphasis on coherence and diversity Riaz, Ayesha; Abdulkader, Omar; Ikram, Muhammad Jawad; Jan, Sadaqat
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1933-1948

Abstract

Topic modeling (TM) is an unsupervised technique used to recognize hidden or abstract topics in large corpora, extracting meaningful patterns of words (semantics). This paper explores TM within data mining (DM), focusing on challenges and advancements in extracting insights from datasets, especially from social media platforms (SMPs). Traditional techniques like latent Dirichlet allocation (LDA), alongside newer methodologies such as bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPT), and extra long-term memory networks (XLNet) are examined. This paper highlights the limitations of LDA, prompting the adoption of embedding-based models like BERT and GPT, rooted in transformer architecture, offering enhanced context-awareness and semantic understanding. The paper emphasizes leveraging pre-trained transformer-based language models to generate document embedding, refining TM and improving accuracy. Notably, integrating BERT with XLNet summaries emerges as a promising approach. By synthesizing insights, the paper aims to inform researchers on optimizing TM techniques, potentially shifting how insights are extracted from textual data.
Co-Authors Abdulkader, Omar Abdulkader, Omar Ahmed Jan, Sadaqat Riaz, Ayesha