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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Constructing dynamic XOR charts for block ciphers using hadamard matrices Phuong, Truong Minh; Luong, Tran Thi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1642-1651

Abstract

Block ciphers are vital for modern encryption, ensuring the security of digital communications. Currently, powerful attacks target block ciphers, prompting researchers to propose ideas to enhance their cryptographic strength. One notable concept involves making components dynamic and dependent on a secret key, with limited attention given to the dynamic AddRoundKey operation. In this article, we introduce the definitions of some Hadamard matrix forms like B_had, N_had, and NB_had matrices. Subsequently, we present an algorithm for generating key-dependent XOR charts to create a key-dependent AddRoundKey operation based on these matrices. We then construct a dynamic AES block cipher by applying the proposed AddRoundKey operation to AES. We implement the dynamic AES algorithm, assess its security, and evaluate AES and the advanced AES using NIST’s statistical standards. The dynamic AES algorithm exhibits improved resistance against strong block cipher attacks compared to conventional AES.
Core machine learning methods for boosting security strength for securing IoT Pavithran, Sneha Nelliyadan; Gorabal, Jayanna Veeranna
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1891-1899

Abstract

Internet-of-things (IoT) revolutionized the mechanism of larger scale of network system offering more engaged, automated, and resilient data dissemination process. However, the resource-limited IoT devices potentially suffers from security issues owing to various inherent weakness. Artificial intelligence (AI) and machine learning (ML) has evolved more recently towards boosting up the security features of IoT offering a secure environment with higher privacy. Till date, there are various review papers to discuss elaborately security aspect of an IoT; however, they miss out to present the actual gap existing between commercial available products and research-based models. Hence, this paper contributes towards discussing the core taxonomy of evolving security methods using ML along with their research trend to offer better insight to existing state of effectiveness. The study further contributes towards highlighting the potential trade-off between the real-world solution and on-going ML based approaches.
Load frequency control for multi-area power system with two-source using sliding mode control Thai Phan, Quoc; Lam-The Tran, Thinh; Tuan Le, Phat; Bao Ho, Dinh; Van Huynh, Van
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1449-1458

Abstract

A consistent electrical supply relies on the stability of power systems. In changing load conditions, control methods like load frequency control (LFC) are essential for safeguarding its stability. Conventional methods of LFC frequently encounter uncertainties in the system, external disruptions, and nonlinearities. This article introduces a more sophisticated method for managing load frequency and improving LFC in power systems through the utilization of sliding mode control (SMC). SMC provides strong stability and resilience against nonlinearities and disturbances, making it a promising method to overcome the drawbacks of traditional control techniques. We offer an in-depth examination of the second-order-integral SMC (SOISMC) method specifically designed for LFC, covering the creation and execution of the control algorithm. The method being suggested utilizes a sliding/gliding surface to maintain the system trajectories as continuous on the surface even with changes in parameters and external disturbances. Simulation results show big enhancements in frequency stability and system performance when compared to conventional proportional-integral-derivative (PID) controllers. The article also features a comparison between SOISMC and other contemporary control methods, emphasizing its strength in terms of resilience and flexibility.
Mechanized network based cyber-attack detection and classification using DNN-generative adversarial model Mahesh, Katikam; Rao, Kunjam Nageswara
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1755-1764

Abstract

These days almost everything is internet. Cyberattacks are the world's most pressing issues. Due to these attacks, Computer systems can be rendered inoperable, disrupted, destroyed or controlled via cyberattacks. Additionally, they can be used to steal, modify, erase, block, or alter data. Most organizations are facing this Issue and lose financially as well as in data security, there are numerous conventional intrusion detection systems (IDS) and firewalls are illustrations for network security tools which are not able to classify and detect different types of attacks in network. With machine learning approach using the Dataset KDD_CUP 99 as input, the synthetic minority oversampling technique (SMOTE) is one of the most often used oversampling methods for addressing imbalance issues. The proposed hybrid deep neural network (DNN), generative adversarial network (GAN), and exhaustive feature selection (EFS) can detect and classify several attack types including R2L, U2R, Probe, denial of service (DoS), and normal attacks types and inform to administrator to ring alarm sound to control and monitor network traffic in dynamically typed networks.
Texture-based two-stage shot boundary detection in videos Anitha, S.; Kavitha, J.; Devaraj, G. Prince; Mall, Shachi; Christal Mary, S. Suma; R, Ezhil
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1955-1963

Abstract

In recent years, shot boundary detection (SBD) has become an essential component of video processing, enabling applications such as video indexing, summarization, and content retrieval. However, the task remains challenging due to frequent false positive detections caused by illumination variations, motion changes, and diverse editing effects. To address these challenges, this paper presents a novel two-stage SBD framework that leverages local quad pattern (LQP) histogram features for precise transition detection. In the first stage, histogram feature vectors are derived by counting the occurrences of LQP codes (−1, +1, 1, 0), and abrupt transitions are identified using the Euclidean distance between consecutive frames. In the second stage, mean values of each histogram bin are computed for consecutive frames, and a similar distance-based approach is applied to refine detection accuracy. A transition frame is confirmed as a shot boundary only if both stages detect it, thereby reducing false positives. The proposed method is evaluated on the TRECVid 2001 and 2007 benchmark datasets, and experimental results demonstrate its superior performance compared to existing algorithms.
Early detection of food safety risks using BERT and large language models Gasbaoui, Mohammed El Amin; Benkrama, Soumia; Bendjima, Mostefa
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1683-1692

Abstract

Sentiment analysis can be a powerful tool in safeguarding public health. This allows authorities to investigate and take action before a foodborne illness outbreak spreads. This paper introduces a novel system that proactively empowers restaurants to identify potential food safety hazards and hygiene regulation violations. The system leverages the power of natural language processing (NLP) to analyze Arabic restaurant reviews left by customers. By fine-tuning a pre-trained BERT mini-Arabic model on three targeted datasets: Sentiment Twitter Corpus, an Algerian dialect dataset, and an Arabic restaurant dataset, the system achieves an impressive accuracy of 91%. Additionally, the system caters to spoken feedback by accepting audio reviews. We utilized Whisper AI for accurate text transcription, followed by classification using a fine-tuned Gemini model from Google on Algerian local comments and others generated using large language models (LLMs) through few-shot learning techniques, reaching an accuracy of 93%. Notably, both models operate independently and concurrently. Leveraging RESTful APIs, the system integrates the solved sub-solutions from each microservice into a fusion layer for a comprehensive restaurant evaluation. This multifaceted approach delivers remarkable results for both modern standard Arabic (MSA) and the Algerian dialect, demonstrating its effectiveness in addressing restaurant food safety concerns.
Jellyfish optimized deep learning framework for cache pollution attack detection in NDN environment Babu, Varghese Jensy; Marianthiran, Victor Jose
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1913-1922

Abstract

Named data networking (NDN) is a promising paradigm that replaces the traditional connection-based model with a content-based approach for future Internet infrastructures, allowing data retrieval by unique names. However, NDN faces threats like cache pollution attacks (CPA) which can lead to increased cache misses and data retrieval delays, and pose significant risks to its efficiency and security. In this paper, a novel jellyfish optimized deep learning (DL) framework for cache pollution attack detection in NDN environment (DSODAL) technique has been proposed to detect the CPA attack with high accuracy. To detect CPA in NDN, a dual-gate attention-based long short-term memory (LSTM) (DA-LSTM) network is used which is optimized using the jellyfish search optimization (JSO) algorithm. The DA-LSTM analyzes request sequences to identify malicious patterns, enhancing cache pollution detection. Nodes manage these requests using the content store (CS) for caching frequently accessed data, optimizing retrieval efficiency, and the pending interest table (PIT) to track and process incoming requests. The DA-LSTM analyzes request sequences to identify malicious patterns and detect CPA attacks. The DSODAL approach performance is evaluated using accuracy, precision, recall, F1-score, average delay time, and mean square error (MSE). The DSODAL model advances the overall accuracy by 1.74%, 2.34%, and 2.7%, over existing HCDLP, ACISE, and AHISM techniques.
Combination of MLF-VO-F and loss functions for VOE from RGB image sequence using deep learning Le, Van-Hung; Do, Huu-Son; Nguyen, Thi-Ha-Phuong; Nguyen, Van-Thuan; Do, Tat-Hung
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1571-1586

Abstract

Visual odometry estimation (VOE) is important in building navigation and pathfinding systems. It helps entities find their way and estimate paths in the environment. Most of the computer vision (CV)-based VOE models are usually evaluated and compared on the KITTI dataset. Multi-layer fusion framework (MLF-VO-F) has had good VOE results from red, green, and blue (RGB) image sequence in Jiang et al. study, using the DeepNet to extract the low-level textures, edges, and deeper high-level semantic features for estimating motion between consecutive frames. This paper proposed a combined model of MLFVO-F as a backbone and loss functions (LFs) (LMSE, LMSE−L2, LCE, and Lcombi) to optimize and supervise the training process of the VOE model. We evaluated and compared the effectiveness of LFs for VOE based on the KITTI and TQU-SLAM datasets with the original MLF-VO-F. From there, choose the appropriate LF combined with the backbone for VOE. The evaluation results on the KITTI dataset show that LCE(RT E is 0.075m, 0.06m on the Seq. #9, Seq. #10, respectively), and Lcombi (trel is 2.21%, 2.67%, 3.59%, 1.01%, and 4.62% on the Seq. #4, Seq. #5, Seq. #6, Seq. #7, Seq. #10, respectively) have the lowest errors and LMSE has the highest errors (AT E is 133.36m on the Seq. #9).
Optimizing supervised learning model for thermal comfort and air quality Sibyan, Hidayatus; Hermawan, Hermawan; Nurhidayati, Ely
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1795-1806

Abstract

Thermal comfort and indoor air quality are essential factors that directly influence occupants’ health and activity efficiency. Ensuring optimal thermal conditions also supports energy-efficient buildings by preventing energy waste. Machine learning models have been extensively applied to classify thermal comfort and air quality, with supervised learning algorithms such as support vector machine (SVM) and K-nearest neighbor (KNN) showing high accuracy. However, no prior study has compared or combined these two models for simultaneous prediction of thermal comfort and air quality, especially in diverse geographical settings. This study aims to develop and compare SVM and KNN to determine the most accurate model for enhancing thermal comfort and air quality in highland and lowland Islamic boarding schools. Using a quantitative approach, we collected datasets from schools in Wonosobo (highland) and Pontianak (lowland). The results show that KNN outperforms SVM in accuracy, precision, and F1-score. Additionally, a hybrid model integrating both algorithms further improves accuracy, achieving 91%. These findings highlight the effectiveness of machine learning in optimizing environmental conditions in educational settings.
Security challenges and strategies for CNN-based intrusion detection model for IoT networks Wan Abdul Rahman, Wan Fariza; Ab Aziz, Nurul Taqiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2012-2022

Abstract

The rapid proliferation of internet-of-things (IoT) networks has revolutionized various industries but has also exposed them to a myriad of security threats. These networks are particularly vulnerable to sophisticated cyber-attacks due to their distributed nature, resource constraints, and the diverse range of connected devices. To safeguard IoT systems, intrusion detection systems (IDS) have emerged as a critical security measure. Among these, convolutional neural network (CNN)-based models offer promising capabilities in recognizing and mitigating malicious activities within IoT environments. This paper addresses the security challenges specific to IoT networks and explores the critical aspects of identifying malicious packets that threaten their integrity. It also delves into the general challenges associated with implementing IDS in IoT settings, such as the need for real-time detection, resource efficiency, and adaptability to evolving threats. The discussion extends to potential strategies for enhancing CNN-based IDS. The paper concludes by summarizing the key findings and proposing directions for future research to overcome the identified challenges, ultimately contributing to the development of more robust and effective IDS solutions for securing IoT networks.

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