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Botnet detection: a system for identifying DGA-based botnets using LightGBM

10.11591/ijeecs.v41.i2.pp833-844
Mumtazimah Mohamad , Nazirah Abd Hamid , Sanaa A. A. Ghaleb , Siti Dhalila Mohd Satar , Suhailan Safei , Wan Mohd Amir Fazamin Wan Hamzah , Lim En En
Botnets present a major challenge to detecting anomalies in domain generation algorithms (DGAs). Botmasters use DGAs to create numerous domain names to communicate with command-and-control servers, complicating the detection process. Traditional blacklisting methods struggle to effectively identify anomalous DGA domain names amid the vast number of randomly generated domains, leading to a greater risk of detection being evaded. The proliferation of DGA-based botnets has created an urgent need for robust detection methods. Various techniques and attributes have been utilised to categorise different DGA families, yet the dynamic nature of DGA domain names renders the current blacklisting algorithms ineffective. Additionally, the dynamic characteristics of DGAs further complicate classification, emphasising the need for machine learning models to improve detection accuracy and enhance cyber defence. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection.
Volume: 41
Issue: 2
Page: 833-844
Publish at: 2026-02-01

Scalable resume screening using large language model Meta AI version 3

10.11591/ijai.v15.i1.pp953-961
Asmita Deshmukh , Anjali Raut , Vedant Deshmukh
This research paper explores the use of large language model Meta AI 3 (LLaMA 3) for automating the resume screening process. Traditional resume screening methods that rely on keyword searching and human review can be inefficient, biased, and fail to identify qualified candidates. LLaMA 3, trained on large-scale text datasets, has the potential to accurately analyze resumes by understanding context and semantic details beyond simple keyword matching.The study presents a system that converts resume PDFs to text, inputs the text along with the job description into the LLaMA 3 model, and generates a ranked list of candidates with reasoning for their job fit. This discusses the data preparation, model setup, and performance evaluation of this system. Results show LLaMA 3 can rapidly process batches of resumes while reducing human bias in the screening process. The system aims to streamline hiring by automating the initial resume screening stage to surface top candidates for further in-depth evaluation. Key benefits include improved accuracy in identifying relevant skills, reduced bias compared to human screeners, and significant time savings for recruiters. The paper also examines ethical considerations around using AI for hiring decisions. Overall, this work demonstrates the promising application of large language models (LLMs) like LLaMA 3 to transform and enhance resume screening practices.
Volume: 15
Issue: 1
Page: 953-961
Publish at: 2026-02-01

Improving efficiency of autism detection based on facial image landmarks

10.11591/ijai.v15.i1.pp766-779
Nguyen Trong Tung , Ngo Duc Vinh , Ha Manh Toan , Do Nang Toan
Autism is a serious mental health problem with long-term effects on life. Therefore, early diagnosis is a topical issue for effective treatment. This study proposes a novel facial landmark transformation-based data augmentation method that allows for the generation of geometric transformations related to facial geometry. This method increases the generalizability and provides a perspective on the role of facial regions in autism detection. The proposed augmentation method ensures the generation of variants that are consistent with the facial image structure and the nature of the facial image. Next, conduct a comprehensive and comparative study with EfficientNet-B0, EfficientNet-B4, ResNet-18, ResNet-50, ResNet-101, MobileNet-V2, DenseNet-121 and DenseNet-201. Also analyze the model's attention over the main regions of the face that are related to facial landmarks. The results clearly show that the models trained with the proposed method outperform the default augmentation method. Specifically, when averaging the measures across the tested models, the results are 0.905417 for accuracy, 0.962133 for area under the curve (AUC), 0.9198 for precision, 0.888333 for recall, and 0.903678 for F1-score. Furthermore, when analyzing the gradient-weighted class activation mapping (Grad CAM) heatmaps, the high-value regions are clearly concentrated on the main areas of the face. Source code is published on GitLab platform.
Volume: 15
Issue: 1
Page: 766-779
Publish at: 2026-02-01

Contextualized clinical anomaly detection with explainable AI and patient modeling

10.11591/ijeecs.v41.i2.pp614-623
Amel Elketroussi , Bachir Djebbar , Ibtissem Bekkouche
This study aims to reduce alarm fatigue and improve the clinical relevance of alerts in intensive care by combining sequential modeling, patient contextualization, explainable artificial intelligence (XAI), and probability calibration. To this end, we leverage the adult cohorts from MIMIC-III/IV, segmented into four-hour windows, explicitly handling missing data and constructing a context vector that integrates demographics, comorbidities, and therapeutic interventions. The approach relies on a tabular autoencoder, an long short-term memory (LSTM) autoencoder, and a transformer, complemented by an adjustment layer based on auditable clinical rules, local explanations (LIME/SHAP), and post-hoc calibration (temperature scaling). Evaluation involves receiver operating characteristic (ROC)/precision–recall (PR) area under the curve (AUC), F1-score, sensitivity and specificity, as well as calibration metrics (ECE, Brier score), alert burden, ablation studies, robustness tests, and subgroup fairness analyses. Across all experiments, the complete model (+Context+XAI+Calibration) outperforms baselines in AUPRC and F1, reduces alert burden, and improves calibration while providing understandable explanations. Specifically, the proposed model improves ROC AUC from 0.74 to 0.89 and reduces alert burden by approximately one third compared to clinical thresholds.
Volume: 41
Issue: 2
Page: 614-623
Publish at: 2026-02-01

The Bender’s decomposition model to optimize temporary waste disposal sites based on general algebraic modeling system

10.11591/ijeecs.v41.i2.pp666-679
Sisca Octarina , Fitri Maya Puspita , Endro Setyo Cahyono , Evi Yuliza , Pebriyanti Simanjuntak , Siti Suzlin Supadi
Waste constitutes a substantial problem in urban and residential locales, as the volume of refuse escalates in tandem with population increase, deteriorating community quality of life. One solution to this problem is to provide temporary waste disposal sites (TWDS). This research discussed optimizing TWDS in the Sukarami Subdistrict, Palembang City, which consists of seven villages. The current TWDS in the Sukarami Subdistrict is irregular, with some sites located close together and others far apart. The optimization problem is solved by formulating the set covering problem (SCP) model, namely the set covering location problem (SCLP), the p-Median problem, and the Bender’s decomposition model. All models were solved using the general algebraic modeling system (GAMS) software. The research introduces a Bender’s decomposition model based on the SCLP model. The Sukarami Subdistrict has 29 TWDS located in only five villages. Using the SCLP and Bender’s decomposition models, the study identified 19 optimal TWDS in the Sukarami Subdistrict. Based on the solution of the p-Median problem, there are seven TWDS that can meet each village’s demand. This study recommends the optimal TWDS obtained from the Bender’s decomposition model. Additionally, two TWDS are recommended to be added, each in Sukodadi and Talang Betutu villages.
Volume: 41
Issue: 2
Page: 666-679
Publish at: 2026-02-01

Joint angle prediction and joint-type classification in human gait analysis using explainable deep reinforcement learning

10.11591/ijeecs.v41.i2.pp564-578
Deepak N. R. , Soumya Naik P. T. , Ambika P. R. , Shaik Sayeed Ahamed
Human gait analysis is a key component of rehabilitation, prosthetics, and sports science, especially for clinical evaluation and the development of adaptive assistive technologies. Accurate joint-angle estimation and dependable joint-type classification remain difficult because of the complex temporal behavior of gait signals and the limited interpretability of many deep learning (DL) approaches. While recent techniques have enhanced predictive accuracy, their clinical applicability is often limited by insufficient transparency and adaptability in learning mechanisms. To overcome these limitations, this work proposes an integrated framework that unifies DL, reinforcement learning (RL), and explainable artificial intelligence (XAI). Stochastic depth neural networks (SDNN) are applied for joint-angle regression, whereas deep feature factorization networks (DFFN) are used for multi-class joint-type classification. Optimization is achieved using Q-learning (QL) and mutual information maximization (MIM), ensuring stable convergence and improved learning efficiency. To improve interpretability, the framework incorporates counterfactual and contrastive explanations, feature ablation studies, and prediction probability analysis. Experimental findings show that the SDNN MIM model attains an R2 score of 0.9881, with RL rewards increasing from 0.997 to 0.999 during regression training. For joint-type classification, the DFFN MIM model achieves an accuracy of 0.95, with reward values improving from 0.90 to 0.98. These results demonstrate the effectiveness of the proposed framework in delivering accurate and interpretable gait predictions, supporting its relevance to biomechanics, healthcare, personalized rehabilitation, and intelligent assistive systems.
Volume: 41
Issue: 2
Page: 564-578
Publish at: 2026-02-01

A hybrid edge–cloud computing framework for low-latency, energy-efficient, and sustainable smart city applications

10.11591/ijeecs.v41.i2.pp791-799
Kamal Saluja , Tanya Khaneja , Sunil Gupta , Reema Goyal , Wai Yie Leong
Smart-city applications demand ultra-low latency, high reliability, and sustainable operation, which are difficult to achieve using cloud-only or edge-only computing paradigms. This study suggests a carbon-conscious architecture for managing smart cities’ intelligent job offloading between the edge and the cloud. This is made possible by the Internet of Things and driven by reinforcement learning (RL). A deep Q-network (DQN) is used to dynamically assign tasks to cloud servers and edge nodes based on how much energy they use, how long it takes to send data over the network, and how much bandwidth they have. A lightweight permissioned blockchain layer makes sure that data is correct across all of its parts, and carbon-aware scheduling puts low-carbon resources first. EdgeCloudSim is used to test the system with real-world smart city workloads. When compared to systems that simply use the cloud, the proposed solution showed a 64.6% drop in average latency, a 24.2% drop in energy use, and a 15% drop in carbon emissions. Combining artificial intelligence (AI)-driven orchestration with scheduling that takes sustainability into account in a hybrid edge-cloud environment yields positive outcomes.
Volume: 41
Issue: 2
Page: 791-799
Publish at: 2026-02-01

Enhancing industrial cybersecurity via IoT device-trusted remote attestation framework with zero trust architecture in brewery operations

10.11591/ijeecs.v41.i2.pp720-730
Muhammad Salman , Alan Budiyanto
The rapid expansion of industrial internet of things (IIoT) adoption in Industry 4.0 has improved automation and real-time control yet simultaneously increased security risks in operational technology (OT) environments, where device integrity and system reliability are critical. Existing attestation approaches such as SAFEHIVE, SEDA, CRA, and ERASMUS provide scalable verification capabilities but still lack continuous hardware-rooted validation and adaptive access control required for real-time industrial systems. To address this gap, this study proposes a hybrid cybersecurity framework that integrates IoT device-trusted remote attestation (ID-TRA) based on trusted platform module (TPM) with zero trust architecture (ZTA) to ensure continuous device trustworthiness in brewery operations. The framework was implemented on an industrial testbed with programmable logic controllers (PLCs), edge devices, and industrial switches, and it was evaluated through measurements of attestation latency, false positive rate, communication overhead, and TPM resource utilization. Experimental results show that the framework achieves an average attestation latency of 250 ms, a false positive rate below 2%, and a communication overhead of only 1.1%, while TPM resource usage remains within acceptable bounds (62% CPU and 48 MB RAM). These outcomes demonstrate that the proposed solution can reliably detect unauthorized firmware modifications, prevent compromised devices from accessing critical network zones, and maintain compatibility with real-time control processes. Overall, the integration of ID-TRA and ZTA enhances device-level assurance and strengthens industrial cybersecurity resilience against firmware tampering, replay attacks, and unauthorized lateral movement.
Volume: 41
Issue: 2
Page: 720-730
Publish at: 2026-02-01

A new hybrid model based on machine learning and fuzzy logic for QoS enhancing in IoT

10.11591/ijeecs.v41.i2.pp624-632
Oussama Lagnfdi , Marouane Myyara , Anouar Darif
The fast expansion of internet of things (IoT) devices presents a more complicated scenario for maintaining a stable quality of service (QoS), which would guarantee the network’s dependable operation. The emergence of increasingly complex applications that call for additional devices makes this even more crucial. Adaptive intelligence solutions that guarantee optimal network behavior are therefore required. This paper presents a hybrid optimized solution for a three-layer IoT network that models the application, network, and perception layers of an IoT network using machine learning and fuzzy logic (FL). This method guarantees optimal QoS prediction with improved network adaptability by using fuzzy membership parameters. When the number of devices increases from 100 to 1,500, FLGA maintains an average QoS of 95% to 87%, while FL maintains 84% and RANDOM maintains 79%. At the application level, genetic algorithm (GA) continues to outperform RANDOM by 15.57% and FL by 6.32%. The goal of this paper is to provide a solid network solution that could enhance the consistency of QoS performance in order to combat the increasingly complex scenario of an IoT network.
Volume: 41
Issue: 2
Page: 624-632
Publish at: 2026-02-01

Enhanced soil moisture sensing using graphene-coated copper electrodes

10.11591/ijeecs.v41.i2.pp470-477
Nuralam Nuralam , Rizdam Firly Muzakki , Sri Lestari Kusumastuti
Soil moisture monitoring is essential for precision agriculture to optimize irrigation and increase crop productivity. Traditional conductivity-based sensors often face limitations such as low sensitivity, slow response, and measurement instability. This study presents a simple and effective enhancement method by applying a graphene coating on copper electrodes using the drop casting technique. Experimental evaluations were conducted on natural soil samples at varying moisture levels. The graphene-coated sensor exhibited a significantly higher sensitivity of 23.0 Ω/% compared to 12.0 Ω/% for the uncoated sensor, a faster response time of approximately 5 seconds, and improved measurement consistency with a reduced standard deviation of ±15 Ω. Graphene's superior electrical conductivity and strong water affinity are key factors contributing to this performance improvement. These findings indicate that graphene-coated sensors offer a promising solution for reliable, cost-effective soil moisture monitoring in smart farming systems.
Volume: 41
Issue: 2
Page: 470-477
Publish at: 2026-02-01

Intelligent cybersecurity framework for real-time threat detection and data protection

10.11591/ijeecs.v41.i2.pp504-514
Gunti Viswanath , Kurapati Srinivasa Rao
Organizations operating across cloud, mobile, and enterprise environments are increasingly exposed to sophisticated cyberattacks that traditional rule-based security systems struggle to detect in real time. These legacy approaches lack adaptability, making it difficult to continuously monitor distributed networks, identify anomalies, and prevent zero-day threats before sensitive data is compromised. To address these challenges, this paper proposes an intelligent cybersecurity framework that integrates real-time network monitoring with AI/ML-based anomaly detection models. The framework utilizes structured preprocessing, feature engineering, and supervised learning on the UNSW-NB15 dataset (version 2015, Cyber Range Lab) to enhance detection accuracy and reduce response time. The experimental setup evaluates multiple ML classifiers using stratified train- test splitting and 5-fold cross-validation, ensuring robust performance validation. Experimental results show that the random forest (RF) model achieves 94.28% accuracy, a 2.93% false-positive rate, and an average detection time of 0.41 seconds, outperforming other baseline models. In addition to the detection layer, the framework incorporates mobile device management (MDM) controls and cloud-storage policy enforcement to strengthen organizational security posture. The main contributions of this work include: i) a unified AI/ML-driven anomaly detection model, ii) integration of MDM and cloud policy enforcement for end-to-end protection, and iii) improved empirical performance validated using a benchmark cybersecurity dataset. This combined architecture significantly enhances real-time threat identification and reduces alert latency, supporting a more security-aware and resilient enterprise environment.
Volume: 41
Issue: 2
Page: 504-514
Publish at: 2026-02-01

Robust palmprint biometric solution for secure mobile authentication

10.11591/ijeecs.v41.i2.pp680-689
Son Nguyen , Arthorn Luangsodsai , Pattarasinee Bhattarakosol
Smartphones increasingly rely on biometric authentication for access to financial and personal services, creating a need for palmprint recognition that is accurate, fast, and deployable on device. This paper proposes an end-to-end smartphone palmprint authentication framework that integrates guided mobile image capture, landmark-based region-of-interest (ROI) extraction, and compact embedding inference. A ResNet-18 teacher is first trained with self-supervised contrastive learning to reduce dependence on labeled biometric data, then distilled into a lightweight MobileNetV3 student for efficient mobile deployment. The learned embeddings support both on device verification and large-scale identification using an approximate nearest neighbor index (FAISS). Experiments on a public Kaggle palm dataset achieve 99.2% accuracy with a 0.15% equal error rate (EER). On an iPhone 13, the end-to-end pipeline runs in 87.0 ms with a 12.4 MB student model. For a 1 million-entry gallery, FAISS provides 32 ms query latency while maintaining 99.5% Recall@1. Limitations include evaluation under mostly controlled capture conditions and the absence of an explicit liveness or presentation attack detection (PAD) module; future work will address unconstrained testing and anti-spoofing integration.
Volume: 41
Issue: 2
Page: 680-689
Publish at: 2026-02-01

ETV: efficient text vision for text localization in natural scene images

10.11591/ijeecs.v41.i2.pp812-822
Suman Suman , Champa H. N.
In the current digital era, the extraction and comprehension of textual information from images have emerged as pivotal tasks. With the exponential growth of text documents, efficient processing and analysis have become imperative. However, text localization in images remains challenging due to complex backgrounds, uneven illumination, diverse text styles, and perspective distortions, rendering traditional optical character recognition (OCR) techniques inadequate. To address these challenges, this paper proposes an integrated method named efficient text vision (ETV). ETV combines the OCR capabilities of Tesseract with the efficient and accurate scene text detector (EAST) algorithm, supplemented by nonmaximum suppression (NMS). The Tesseract OCR component facilitates the extraction and identification of individual characters, while EAST excels in the efficient detection and localization of complete text sections. The incorporation of NMS enhances localization accuracy by eliminating redundant or overlapping bounding boxes.
Volume: 41
Issue: 2
Page: 812-822
Publish at: 2026-02-01

Control of multi-level NPC inverters in PV/grid systems using ADRC and MADRC

10.11591/ijeecs.v41.i2.pp456-469
Gherici Dinar , Ahmed Tahour
Grid-connected photovoltaic (PV) systems consist of solar panels that convert sunlight into electrical energy, interconnected directly with the utility grid. These systems comprise several key components: PV, multilevel, controllers, and grid interface equipment. In this context, fivelevel inverters are increasingly favoured over three-level inverters due to their ability to reduce total harmonic distortion (THD), improve efficiency, and ensure better power quality in grid-connected applications. This research presents a three-level enhanced control scheme aimed at optimizing the performance of a grid-connected photovoltaic system with a five-level inverter. A fractional-order proportional-integral (FOPI) controller is utilized for maximum power point tracking (MPPT) to ensure precise tracking under variable irradiance conditions. At the grid-interface stage, a modified active disturbance rejection controller (MADRC) is developed for grid-interface, featuring an inner loop for DC-link voltage regulation based on Lyapunov theory, leading to improved dynamic performance with lower THD of the grid current and enhanced efficiency. Simulation results highlight the effectiveness of the proposed system. Compared with the FOPI-ADRC, a three-level configuration (0.38% THD), the proposed FOPI-MADRC with a five-level inverter achieves superior performance, with only (0.22% THD). These results confirm the advantages of combining advanced control strategies with multilevel inverter technology in improving both power quality and system efficiency.
Volume: 41
Issue: 2
Page: 456-469
Publish at: 2026-02-01

Engineering intelligence for sustainable and secure digital futures

10.11591/ijeecs.v41.i2.pp453-455
Tole Sutikno
This editorial introduces Volume 41, Number 2 (February 2026) of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), which presents a diverse collection of peer-reviewed articles reflecting recent advances in electrical engineering, electronics, and computer science. The issue highlights the convergence of power and energy systems, artificial intelligence, cybersecurity, the Internet of Things (IoT), and datadriven engineering methodologies in addressing contemporary technological and societal challenges, with key contributions focusing on renewable energy integration, intelligent control strategies, secure and trusted digital infrastructures, smart IoT-based systems, and AI-driven applications in healthcare, finance, industrial automation, and human-centered computing. Particular emphasis is placed on energy efficiency, system resilience, explainable and trustworthy artificial intelligence, and sustainable engineering practices. Collectively, the published works demonstrate how interdisciplinary research can bridge theory and real-world implementation while supporting the United Nations Sustainable Development Goals, including affordable and clean energy, good health and well-being, sustainable cities, responsible consumption, and strong digital institutions. By fostering innovation, cross-domain collaboration, and responsible technology development, this issue of IJEECS aims to advance secure, intelligent, and sustainable engineering solutions that respond to both current demands and future global challenges. This issue further reinforces the journal’s commitment to advancing engineering intelligence that is ethically grounded, environmentally responsible, and resilient by design.
Volume: 41
Issue: 2
Page: 453-455
Publish at: 2026-02-01
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