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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Zhukabayeva, Tamara
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Enhancing internet of things security against structured query language injection and brute force attacks through federated learning Adamova, Aigul; Zhukabayeva, Tamara; Mukanova, Zhanna; Oralbekova, Zhanar
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.pp1187-1199

Abstract

The internet of things (IoT) encompasses various devices for monitoring, data collection, tracking people and assets, and interacting with other gadgets without human intervention. Implementing a system for predicting the development and assessing the criticality of detected attacks is essential for ensuring security in IoT interactions. This work analyses existing methods for detecting attacks, including machine learning, deep learning, and ensemble methods, and explores the federated learning (FL) method. The aim is to study FL to enhance security, develop a methodology for predicting the development of attacks, and assess their criticality in real-time. FL enables devices and the aggregation server to jointly train a common global model while keeping the original data locally on each client. We demonstrate the performance of the proposed methodology against structured query language (SQL) injection and brute force attacks using the CICIOT2023 dataset. We used accuracy and F1 score metrics to evaluate the effectiveness of our proposed methodology. As a result, the accuracy in predicting SQL injection reached 100%, and for brute force attacks, it reached 98.25%. The high rates of experimental results clearly show that the proposed FL-based attack prediction methodology can be used to ensure security in IoT interactions.
Tackling the anomaly detection challenge in large-scale wireless sensor networks Zhukabayeva, Tamara; Adamova, Aigul; Zholshiyeva, Lazzat; Mardenov, Yerik; Karabayev, Nurdaulet; Baumuratova, Dilaram
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.pp2479-2490

Abstract

One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Assessing the knowledge and practices of internet of things security and privacy among higher education students Adamova, Aigul; Zhukabayeva, Tamara; Zhartybayeva, Makpal; Zhumabayeva, Laula
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4074-4086

Abstract

When multiple internet of things (IoT) devices interact, there are risks of privacy breaches, personal data leaks, various attacks, and device manipulation. Security is one of the most important technological research problems that currently exist for the IoT. The main purpose of the present paper is to determine the level of awareness of university students about existing security issues when using IoT devices. The paper presented the methodology of the survey. A questionnaire was developed covering four areas, such as fact-finding about general concepts of the IoT, security measures when using IoT devices, security threats and the presence of vulnerabilities of IoT devices, general policies, practices and shared responsibilities. A methodology for calculating the Awareness Level Index is proposed. This study has potential limitations. The effect estimates in the model are based on a survey of undergraduate and master’s degree students in “Computer Science” and “Software Engineering” within several universities. A total of 370 undergraduate and master’s students participated in the survey. The data processing resulted in the development of recommendations and suggested measures. This study will be useful for both stakeholders and researchers to develop effective strategies and make informed decisions.
Co-Authors Abdildayeva, Assel Adamova, Aigul Baumuratova, Dilaram Berlikozha, Bauyrzhan Buja, Atdhe Dias, Oliver Karabayev, Nurdaulet Mardenov, Erik Mardenov, Yerik Mukanova, Zhanna Nurlan, Zhanserik Oralbekova, Zhanar Pacolli, Melinda Serek, Azamat Ven-Tsen, Khu Zhamanov, Azamat Zhartybayeva, Makpal Zholshiyeva, Lazzat Zhumabayeva, Laula