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All Journal Bulletin of Electrical Engineering and Informatics
Singh, Manmeet Mahinderjit
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Electrical & Electronics Engineering

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Privacy during epidemic of COVID-19: a bibliometric analysis Ali, Auwal Shehu; Zaaba, Zarul Fitri; Singh, Manmeet Mahinderjit
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4460

Abstract

1,226 articles on privacy and COVID-19 were published by authors from 69 countries in this year's issue. COVID 19's privacy is now the focus of many researchers' attention. The present body of knowledge on privacy for COVID-19 digital technologies has been thoroughly analyzed, and a concise overview of research status and future developments can be gleaned. This paper conducted a bibliometric examination of privacy using the Scopus dataset. Utilizing VOSviewer software, the relevant literature papers published on this topic were examined to determine the field's development history, research hotspots, and future directions. Over time, there has been a rise in the number of studies published in privacy for COVID-19, particularly after 2020, and the growth rate has been steadily increasing. Regarding published research, the United States and China lead the pack. These articles appeared in primarily English-language journals and conference proceedings. Privacy and COVID-19 research was mostly computer science. The most used terms in privacy and COVID-19 were data privacy and humans. This paper examines the evolution of privacy and COVID-19 research and indicates current research priorities and future research goals. Furthermore, the privacy and COVID-19 study seem to be a promising sphere as this study identifies 26 domains.
User authentication using gait and enhanced attribute-based encryption: a case of smart home Pin, Lim Wei; Singh, Manmeet Mahinderjit
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5347

Abstract

With the increasing popularity of the internet of things (IoT) application such as smart home, more data is being collected, and subsequently, concerns about preserving the privacy and confidentiality of these data are growing. When intruders attack and get control of smart home devices, privacy is compromised. Attribute-based encryption (ABE) is a new technique proposed to solve the data privacy issue in smart homes. However, ABE involves high computational cost, and the length of its ciphertext/private key increases linearly with the number of attributes, thus limiting the usage of ABE. This study proposes an enhanced ABE that utilises gait profile. By combining lesser number of attributes and generating a profiling attribute that utilises gait, the proposed technique solves two issues: computational cost and one-to-one encryption. Based on experiment conducted, computational time has been reduced by 55.27% with nine static attributes and one profile attribute. Thus, enhanced ABE is better in terms of computational time.
Enhanced detection of android ransomware families using machine learning and network traffic analysis Singh, Manmeet Mahinderjit; Selvaraj, Kalaivani; Wei, Zhao
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9485

Abstract

Ransomware attacks on Android devices often go undetected until damage occurs, as prevention strategies are limited by inconsistent threat detection and classification. This paper presents a framework for evaluating machine learning models to detect and classify Android ransomware families through network behavioral analysis. The framework extracts discriminative features from network traffic data and segregates them into four optimal clusters using the k-means clustering method. A total of 84 critical network traffic features are identified, including source IP, destination IP, source port, destination port, traffic duration, and the total number of forward and reverse packets. These optimal features are effectively utilized to train well-known machine learning models, including decision trees (DT), random forest (RF), K-nearest neighbors (KNN), support vector machines (SVM), and bagging, to evaluate their accuracy in classifying ransomware families. Simulation results demonstrate that RF achieves the best performance with an accuracy of 95.18%, precision of 95.21%, recall of 95.27%, and F1-score of 95.19%. This framework, focused on network behavioral analysis rather than static or dynamic analysis, provides deeper insights into the behavior and characteristics of ransomware.
Co-Authors Ali, Auwal Shehu Pin, Lim Wei Selvaraj, Kalaivani Zaaba, Zarul Fitri Zhao Wei