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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 : Journal of Robotics and Control (JRC)

 1 
Evaluating Security Mechanisms for Wireless Sensor Networks in IoT and IIoT Zhukabayeva, Tamara; Buja, Atdhe; Pacolli, Melinda
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.21683

Abstract

In the era of interconnected digital ecosystems, the security of Wireless Sensor Networks (WSN) emerges as a pivotal concern, especially within the domains of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT). However, the very nature of WSNs—being distributed, resource-constrained, and often deployed in unattended environments—poses unique cybersecurity challenges.  A main issue and challenge remains their Cybersecurity in communication. In this paper, we provide a systematic review focused on three themes including 1) techniques for secure communication in WSN; 2) algorithms and methods for intrusion detection in WSN; and 3) IoT and IIoT security concerning WSN. It has provided the results of its own for the publications made in the data analysis of three themes. The paper also has a simulation experiment to investigate the behavior of WSNs under sinkhole attacks—one of the prevalent threats to network integrity. Utilizing the Contiki OS Cooja simulator, the experiment carefully evaluates the performance of existing detection algorithms and introduces a novel method for identifying and neutralizing malicious nodes. Our simulation discloses unconventional communication patterns during sinkhole attacks running RPL protocol, emphasizing the effectiveness of our detection mechanisms against cyber threats. Particularly, the introduction of a malicious node (Node 13) significantly disrupted network communication, with traditional security mechanisms failing to immediately detect and isolate the threat. The scope of future research work will include the broader spectrum of cyber threats beyond sinkhole attacks, exploring advanced detection mechanisms, and machine learning-based security protocols for enhanced trust and transparency in WSN communications.
Comprehensive Study on Detecting Multi-Class Classification of IoT Attack Using Machine Learning Methods Zhukabayeva, Tamara; Zholshiyeva, Lazzat; Ven-Tsen, Khu; Adamova, Aigul; Karabayev, Nurdaulet; Mardenov, Erik
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.22819

Abstract

The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The paper presents a novel approach to detect different botnet assaults on IoT devices by utilizing ML methods such as XGBoost, Random Forest, LightGBM, and Decision Tree. These algorithms were examined using the N-BaIoT dataset to classify multi-class botnet attacks and were specifically designed to accommodate the limitations of IoT devices. The technique comprises the steps of data preparation, preprocessing, classifier training, and decision-making. The algorithms achieved high detection accuracy rates: XGBoost (99.18%), Random Forest (99.20%), LGBM (99.85%), and Decision Tree (99.17%). The LGBM model demonstrated exceptional performance. The incorporation of the attack evaluation model greatly enhanced the identification of botnets in IoT networks. The paper displays the efficacy of machine learning techniques in identifying botnet assaults in IoT networks. The models generated exhibit exceptional accuracy and can be seamlessly integrated into existing cybersecurity systems.
Design of QazSL Sign Language Recognition System for Physically Impaired Individuals Zholshiyeva, Lazzat; Zhukabayeva, Tamara; Baumuratova, Dilaram; Serek, Azamat
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.23879

Abstract

Automating real-time sign language translation through deep learning and machine learning techniques can greatly enhance communication between the deaf community and the wider public. This research investigates how these technologies can change the way individuals with speech impairments communicate. Despite advancements, developing accurate models for recognizing both static and dynamic gestures remains challenging due to variations in gesture speed and length, which affect the effectiveness of the models. We introduce a hybrid approach that merges machine learning and deep learning methods for sign language recognition. We provide new model for the recognition of Kazakh Sign Language (QazSL), employing five algorithms: Support Vector Machine (SVM), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN) with VGG19, ResNet-50, and YOLOv5. The models were trained on a QazSL dataset of more than 4,400 photos. Among the assessed models, the GRU attained the highest accuracy of 100%, followed closely by SVM and YOLOv5 at 99.98%, VGG19 at 98.87% for dynamic dactyls, LSTM at 85%, and ResNet-50 at 78.61%. These findings illustrate the comparative efficacy of each method in real-time gesture recognition. The results yield significant insights for enhancing sign language recognition systems, presenting possible advancements in accessibility and communication for those with hearing impairments.
Development of Method to Predict Career Choice of IT Students in Kazakhstan by Applying Machine Learning Methods Berlikozha, Bauyrzhan; Serek, Azamat; Zhukabayeva, Tamara; Zhamanov, Azamat; Dias, Oliver
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25558

Abstract

The growing intricacy of IT education requires resources to aid students in choosing specialized pathways. This study investigates the prediction of specialization preferences among IT students at SDU University in Kazakhstan through the application of machine learning techniques. The research contribution is the development of a predictive model that enhances academic advising by incorporating multiple factors, including academic performance, personality traits, qualifications, and extracurricular involvement. The research examined 692 anonymized student profiles and evaluated the efficacy of five machine learning algorithms: Random Forest, K-Nearest Neighbors, Support Vector Machine, Gradient Boosting, and Naive Bayes. Stratified 10-fold cross-validation was utilized to reduce the risk of overfitting. Gradient Boosting attained a peak accuracy of 99.10% in validation; however, its performance decreased to 92.16% on an independent test set, suggesting overfitting. Naive Bayes exhibited the lowest accuracy, recorded at 35.26%. Logistic regression analysis indicated a statistically significant correlation (p < 0.05) among academic performance, extracurricular involvement, and specialization selection. Personality traits and certifications significantly influenced the prediction process. The findings suggest that although Gradient Boosting demonstrates high effectiveness, the associated risk of overfitting requires additional refinement for practical application. The notable impact of academic performance and extracurricular activities indicates that educational institutions ought to prioritize these elements in student guidance. The incorporation of machine learning-based recommendations into advising frameworks enhances the precision of specialization predictions, thereby improving student decision-making and career alignment.
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