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A comparative study of deep learning-based network intrusion detection system with explainable artificial intelligence

10.11591/ijece.v15i4.pp4109-4119
Tan Juan Kai , Lee-Yeng Ong , Meng-Chew Leow
In the rapidly evolving landscape of cybersecurity, robust network intrusion detection systems (NIDS) are crucial to countering increasingly sophisticated cyber threats, including zero-day attacks. Deep learning approaches in NIDS offer promising improvements in intrusion detection rates and reduction of false positives. However, the inherent opacity of deep learning models presents significant challenges, hindering the understanding and trust in their decision-making processes. This study explores the efficacy of explainable artificial intelligence (XAI) techniques, specifically Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), in enhancing the transparency and trustworthiness of NIDS systems. With the implementation of TabNet architecture on the AWID3 dataset, it is able to achieve a remarkable accuracy of 99.99%. Despite this high performance, concerns regarding the interpretability of the TabNet model's decisions persist. By employing SHAP and LIME, this study aims to elucidate the intricacies of model interpretability, focusing on both global and local aspects of the TabNet model's decision-making processes. Ultimately, this study underscores the pivotal role of XAI in improving understanding and fostering trust in deep learning -based NIDS systems. The robustness of the model is also being tested by adding the signal-to-noise ratio (SNR) to the datasets.
Volume: 15
Issue: 4
Page: 4109-4119
Publish at: 2025-08-01

Rapid and efficient maximum power point tracking in photovoltaic systems with modified fuzzy logic approach

10.11591/ijece.v15i4.pp3621-3631
El-bot Said , Yassine El Moujahid , Chafik El Idrissi Mohamed , Abdessamad Benlafkih
Photovoltaic systems (PVs) often face difficulties in maximizing their output power and maintaining a stable DC-DC connection voltage, especially under variable weather conditions (VWC). The power produced by photovoltaic panels is very sensitive to changes in sunlight and temperature, which vary throughout the day. This paper presents the design of an intelligent controller approach based on modified fuzzy logic (MFLC), adapted to enable the most effective maximum power point tracking (MPPT) of a photovoltaic solar module. The technique reduces delays in MPPT and sustains efficiency despite changing environmental conditions. A DC-DC boost converter is connected to the photovoltaic solar module, which in turn is linked to a load, and computer simulations using MATLAB/Simulink were used to validate the method's effectiveness. Results reveal that the MFLC controller significantly enhances the efficiency of the PVs, achieving improvements of up to 97.05%, with a rapid settling time of less than 10 milliseconds across all test scenarios.
Volume: 15
Issue: 4
Page: 3621-3631
Publish at: 2025-08-01

Energy-efficient secure software-defined networking with reinforcement learning and Weierstrass cryptography

10.11591/ijece.v15i4.pp4227-4238
Nagaraju Tumakuru Andanaiah , Malode Vishwanatha Panduranga Rao
In the age of rapidly advancing 5G connectivity, artificial intelligence (AI), and the internet of things (IoT), network data has grown enormously, demanding more efficient and secure management solutions. Traditional networking systems, limited by manual controls and static environments, are unable to fulfill the dynamic demands of modern internet services. This paper proposes an innovative software-defined networking (SDN) framework that utilizes exponential spline regression reinforcement learning (ESR-RL) with genus Weierstrass curve cryptography (GWCC) to boost energy efficiency and data security. The ESR-RL algorithm reliably anticipates network traffic patterns, optimizing path selection to enhance routing efficiency while minimizing consumption of energy. GWCC also enables strong encryption and decryption, considerably increasing data security without impacting system performance. To further improve network reliability, the Skellam distributed Siberian TIGER optimization algorithm (SDSTOA) is used to dynamically acquire features and balance loads, resulting in optimal network performance. Extensive simulations show that the proposed framework performs better than existing models in terms of accuracy, precision, recall, F-measure, sensitivity, and specificity. Improvements in latency, turnaround time, and network throughput demonstrate the framework's success. This scalable and adaptive technology establishes a new standard for SDN systems by providing a safe, energy-efficient, and performance-optimized strategy for future network infrastructures.
Volume: 15
Issue: 4
Page: 4227-4238
Publish at: 2025-08-01

Load frequency control for integrated hydro and thermal power plant power system

10.11591/ijece.v15i4.pp3583-3592
Vu Tan Nguyen , Thinh Lam-The Tran , Dao Huy Tuan , Dinh Cong Hien , Vinh Phuc Nguyen , Van Van Huynh
Persistent electrical supply requires the power systems to be stable and reliable. Against varying load conditions, control strategies such as load frequency control (LFC) is a key mechanism to protect its stability. Traditional control strategies for LFC often face challenges due to system uncertainties, external disturbances, and nonlinearities. This paper presents an advanced approach to control load frequency and enhancing LFC in power systems by using sliding mode control (SMC). SMC offers powerful stability and robustness versus nonlinearities and perturbation, making it a promising approach for addressing the limitations of conventional control methods. We contemporary a comprehensive analysis of the SMC approach tailored for LFC, including the strategy and employment of the control algorithm. The proposed method makes use of a sliding/gliding surface to enable the system trajectories to be continuous on this surface despite parameter variations and external disturbances. Simulation results demonstrate significant improvements in frequency stability and system performance compared to conventional proportional-integral-derivative (PID) controllers. The paper also includes a comparative analysis of SMC with other modern control techniques, highlighting its advantages in terms of robustness and adaptability.
Volume: 15
Issue: 4
Page: 3583-3592
Publish at: 2025-08-01

Secure clustering and routing – based adaptive – bald eagle search for wireless sensor networks

10.11591/ijece.v15i4.pp3824-3832
Roopashree Hejjaji Ranganathasharma , Yogeesh Ambalagere Chandrashekaraiah
Wireless sensor networks (WSNs) are self-regulating networks consisting of several tiny sensor nodes for monitoring and tracking applications over extensive areas. Energy consumption and security are the two significant challenges in these networks due to their limited resources and open nature. To address these challenges and optimize energy consumption while ensuring security, this research proposes an adaptive – bald eagle search (A-BES) optimization algorithm enabled secure clustering and routing for WSNs. The A-BES algorithm selects secure cluster heads (SCHs) through several fitness functions, thereby reducing energy consumption across the nodes. Next, secure and optimal routes are chosen using A-BES to prevent malicious nodes from interfering with the communication paths and to enhance the overall network lifetime. The proposed algorithm shows significantly lower energy consumption, with values of 0.27, 0.81, 1.38, 2.27, and 3.01 J as the number of nodes increases from 100 to 300. This demonstrates a clear improvement over the existing residual energy-based data availability approach (REDAA).
Volume: 15
Issue: 4
Page: 3824-3832
Publish at: 2025-08-01

Efficient high-gain low-noise amplifier topologies using GaAs FET at 3.5 GHz for 5G systems

10.11591/ijece.v15i4.pp3833-3842
Samia Zarrik , Abdelhak Bendali , Elmahdi Fadlaoui , Karima Benkhadda , Sanae Habibi , Mouad El Kobbi , Zahra Sahel , Mohamed Habibi , Abdelkader Hadjoudja
Achieving a gain greater than 18 dB with a noise figure (NF) below 2 dB at 3.5 GHz remains a formidable challenge for low-noise amplifiers (LNAs) in sub-6 GHz 5G systems. This study explores and evaluates various LNA topologies, including single-stage designs with inductive source degeneration and cascade configurations, to optimize performance. The single-stage topology with inductive source degeneration achieves a gain of 18.141 dB and an NF of 1.448 dB, while the cascade-stage common-source low-noise amplifier with inductive degeneration achieves a gain of 32.714 dB and a noise figure of 1.563 dB. These results underscore the importance of GaAs FET technology in meeting the demanding requirements of 5G systems, specifically in the 3.5 GHz frequency band. The advancements demonstrated in gain, noise figure, and linearity affirm the viability of optimized LNA topologies for high-performance 5G applications, supporting improved signal quality and reliability essential for modern telecommunication infrastructure.
Volume: 15
Issue: 4
Page: 3833-3842
Publish at: 2025-08-01

Integrating time-frequency features with deep learning for lung sound classification

10.11591/ijece.v15i4.pp3737-3747
Su Yuan Chang , Marni Azira Markom , Zhi Sheng Choong , Arni Munira Markom , Latifah Munirah Kamaruddin , Erdy Sulino Mohd Muslim Tan
Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Volume: 15
Issue: 4
Page: 3737-3747
Publish at: 2025-08-01

A ten-year retrospective (2014-2024): Bibliometric insights into the study of internet of things in engineering education

10.11591/ijece.v15i4.pp4213-4226
Zakiah Mohd Yusoff , Siti Aminah Nordin , Norhalida Othman , Zahari Abu Bakar , Nurlaila Ismail
This article presents a comprehensive ten-year retrospective analysis (2014-2024) of the evolving landscape of internet of things (IoT) studies within engineering education, employing bibliometric insights. The pervasive influence of IoT technologies across diverse domains, including education, underscores the significance of examining its trajectory in engineering education research over the past decade. Recognizing the dynamic nature of this intersection is crucial for educators, researchers, and policymakers to adapt educational strategies to IoT-induced technological shifts. Addressing this imperative, the study conducts a detailed bibliometric review to identify gaps, trends, and areas necessitating further exploration. Methodologically, the study follows a framework involving a comprehensive search of Scopus and Web of Science databases to identify relevant articles. Selected articles undergo bibliometric analysis using the Biblioshiny tool, supplemented by manual verification and additional analysis in Excel. This approach facilitates robust evaluation of citation patterns, co-authorship networks, keyword trends, and publication patterns over the specified timeframe. Anticipated outcomes include the identification of seminal works, key contributors, influential journals, and science mapping. The study aims to unveil emerging themes, track research trends, and provide insights into collaborative networks shaping IoT discourse in engineering education. This analysis offers a roadmap for future research directions, guiding educators and researchers toward fruitful avenues of exploration.
Volume: 15
Issue: 4
Page: 4213-4226
Publish at: 2025-08-01

Assessing the knowledge and practices of internet of things security and privacy among higher education students

10.11591/ijece.v15i4.pp4074-4086
Aigul Adamova , Tamara Zhukabayeva , Makpal Zhartybayeva , Laula Zhumabayeva
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.
Volume: 15
Issue: 4
Page: 4074-4086
Publish at: 2025-08-01

Techno-economic analysis of a 4 MW solar photovoltaic capacity expansion in a remote Indonesian village

10.11591/ijece.v15i4.pp4133-4147
Agie Maliki Akbar , Fahmy Rinanda Saputri , David Tee
As of the end of 2022, Indonesia’s electrification ratio reached 99.63%, reflecting significant progress. However, the province of East Nusa Tenggara lags behind with an electrification ratio below 90%, indicating a considerable gap in energy access. This challenge is particularly evident in Oelpuah village, where frequent power outages occur due to the inadequacy of the existing 5 MW solar farm. This study proposes addressing this shortfall by expanding the solar farm capacity by an additional 4 MW. Comprehensive feasibility studies were conducted, evaluating solar radiation, natural disaster risks, and land use. The analysis, supported by PVSyst simulations, identified a suitable site with high radiation levels, though it is not entirely free from disaster risks. The design requires 13,500 solar panel modules, each with a capacity of 330 Wp, and seven 500 kW inverters. Optimal system performance is achieved with a 15-degree panel tilt and a 0-degree azimuth, aligning with the site's location south of the equator. This expansion could supply electricity to up to 4,014 households, each with a typical power usage of 0.825 kW. The study highlights the need for further research to enhance electricity coverage across Indonesia.
Volume: 15
Issue: 4
Page: 4133-4147
Publish at: 2025-08-01

Adaptive multi-radio quality of service model using neural network approach for robust wireless sensor network transmission in multipath fading environment

10.11591/ijece.v15i4.pp3795-3802
Galang Persada Nurani Hakim , Dian Widi Astuti , Ahmad Firdausi , Huda A. Majid
Wireless sensor network loss in wireless data transmission is one of the problems that needs attention. Interference, fading, congestion, and delay are some factors that cause loss in wireless data transmission. This paper used an adaptive multi-radio model to enhance the wireless data transmission to be more robust to disturbance in a multipath fading environment. A neural network approach was used to generate the adaptive model. If we use 433 MHz as our carrier frequency with 250 kHz bandwidth and 12 spreading factors, we can get signal noise ratio (SNR) for 20 meters at about -9.8 dB. Thus, we can use the adaptive model to enhance the WSN wireless data transmission's SNR to 9 dB, automatically changing the radio configuration to 797.1 MHz frequency, with 378.1 bandwidth and 7.111 for spreading factor. Based on the result, the wireless data transmission link has been successfully enhanced using the proposed adaptive model for wireless sensor networks (WSN) in a multipath fading environment.
Volume: 15
Issue: 4
Page: 3795-3802
Publish at: 2025-08-01

Breast cancer identification using a hybrid machine learning system

10.11591/ijece.v15i4.pp3928-3937
Toni Arifin , Ignatius Wiseto Prasetyo Agung , Erfian Junianto , Dari Dianata Agustin , Ilham Rachmat Wibowo , Rizal Rachman
Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Volume: 15
Issue: 4
Page: 3928-3937
Publish at: 2025-08-01

Multi-layer convolutional autoencoder for recognizing three-dimensional patterns in attention deficit hyperactivity disorder using resting-state functional magnetic resonance imaging

10.11591/ijece.v15i4.pp3965-3976
Zarina Begum , Kareemulla Shaik
Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that develops over time and is typified by impulsivity, hyperactivity, and attention deficiency. There have been noticeable changes in the patterns of brain activity in recent studies using functional magnetic resonance imaging (fMRI). Particularly in the prefrontal cortex. Machine learning algorithms show promise in distinguishing ADHD subtypes based on these neurobiological signatures. However, the inherent heterogeneity of ADHD complicates consistent classification, while small sample sizes limit the generalizability of findings. Additionally, methodological variability across studies contributes to inconsistent results, and the opaque nature of machine learning models hinders the understanding of underlying mechanisms. We suggest a novel deep learning architecture to overcome these issues by combining spatio-temporal feature extraction and classification through a hierarchical residual convolutional noise reduction autoencoder (HRCNRAE) and a 3D convolutional gated memory unit (GMU). This framework effectively reduces spatial dimensions, captures key temporal and spatial features, and utilizes a sigmoid classifier for robust binary classification. Our methodology was rigorously validated on the ADHD-200 dataset across five sites, demonstrating enhancements in diagnostic accuracy ranging from 1.26% to 9.6% compared to existing models. Importantly, this research represents the first application of a 3D Convolutional GMU for diagnosing ADHD with fMRI data. The improvements highlight the efficacy of our architecture in capturing complex spatio-temporal features, paving the way for more accurate and reliable ADHD diagnoses.
Volume: 15
Issue: 4
Page: 3965-3976
Publish at: 2025-08-01

Machine learning approaches to cybersecurity in the industrial internet of things: a review

10.11591/ijece.v15i4.pp3851-3866
Melanie Heier , Penatiyana W. Chandana Prasad , Md Shohel Sayeed
The industrial internet of things (IIoT) is increasingly used within various sectors to provide innovative business solutions. These technological innovations come with additional cybersecurity risks, and machine learning (ML) is an emerging technology that has been studied as a solution to these complex security challenges. At time of writing, to the author’s knowledge, a review of recent studies on this topic had not been undertaken. This review therefore aims to provide a comprehensive picture of the current state of ML solutions for IIoT cybersecurity with insights into what works to inform future research or real-world solutions. A literary search found twelve papers to review published in 2021 or later that proposed ML solutions to IIoT cybersecurity concerns. This review found that federated learning and semi-supervised learning in particular are promising ML techniques being proposed to combat the concerns around IIoT cybersecurity. Artificial neural network approaches are also commonly proposed in various combinations with other techniques to ensure fast and accurate cybersecurity solutions. While there is not currently a consensus on the best ML techniques to apply to IIoT cybersecurity, these findings offer insight into those approaches currently being utilized along with gaps where further examination is required.
Volume: 15
Issue: 4
Page: 3851-3866
Publish at: 2025-08-01

Enhancing voltage stability of transmission network using proportional integral controlled high voltage direct current system

10.11591/ijece.v15i4.pp3593-3602
Chibuike Peter Ohanu , Uche C. Ogbuefi , Emenike Ejiogu , Tole Sutikno
The contingencies experienced in transmission power networks often lead to unstable voltage profiles, challenging grid reliability and stability. This research aim is to enhance voltage stability using a proportional-integral (PI) controlled high voltage direct current (HVDC) system on a real life 330 kV network. The Newton-Raphson (NR) method is used for power flow analysis of the test network, and stability analysis identified Makurdi bus as the candidate bus for improvement due to its low eigenvalue and damping ratio. Application of a balanced three-phase fault at this bus resulted in a minimum voltage of 0.70 per unit (p.u.), falling outside the statutory voltage limit requirements of 0.95 to 1.05 p.u. The PI-based HVDC system was then applied along the Makurdi to Jos transmission line, which has a low loading capacity. The application of this model optimized the system response to disturbances, significantly improve voltage stability and raised the minimum voltage profile on the network to 0.80 p.u. This demonstrates 10% voltage profile improvement from the base case and reaffirms the effectiveness of the PI-based HVDC system in enhancing voltage stability during major disturbances. This research highlights the potential of integrating control systems into power networks to improve voltage stability and ensure reliable operation, even during large disturbances.
Volume: 15
Issue: 4
Page: 3593-3602
Publish at: 2025-08-01
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