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International Journal of Computer Technology and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481899     EISSN : 30481961     DOI : 10.62951
Core Subject : Science,
Computer Science & IT
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. The scope of the this Journal covers the fields of Computer Technology and Science. This journal is a means of publication and a place to share research and development work in the field of technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 51 Documents
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Securing Industrial IoT: Blockchain-Integrated Solutions for Enhanced Privacy, Authentication, and Efficiency Derrick Lim Kin Yeap; Jason Jong Sheng Tat; Jason Ng Yong Xing; Joan Sia Yuk Ting; Mildred Lim Pei Chin; Muhammad Faisa
International Journal of Computer Technology and Science Vol. 1 No. 3 (2024): July : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i3.18

Abstract

The Industrial Internet of Things (IIoT) enhances the connectivity and efficiency of living lifestyles. However, it also comes with significant security vulnerabilities. Traditional authentication methods are often inadequate, leading to IIoT devices opened to security threats. This paper proposes a comprehensive security framework integrating blockchain, cryptographic techniques, smart contracts, and deep learning-based Intrusion Detection Systems (IDS) to tackle the mentioned issue. Blockchain ensures data integrity and prevents tampering through a decentralized ledger. A decentralized device identity management system enhances user verification, while secure communication protocols using Hash-based Message Authentication Codes (HMAC) safeguard data integrity. Smart contracts automate transactions, providing transparent, secure record-keeping without a central authority. The deep learning-based IDS, utilizing Contractive Sparse Autoencoder (CSAE) and Attention-Based Bidirectional Long Short-Term Memory (ABiLSTM) networks, effectively detects cyber threats. Evaluation metrics, including precision, recall, F1-score, and False Acceptance Rate (FAR), demonstrate high accuracy and low false alarm rates across datasets. This framework addresses the need for secure, efficient, and scalable authentication in IIoT, combining blockchain's security features with advanced cryptographic and anomaly detection techniques, offering robust defence against cyber threats.
Enhancing IIoT Security: AI-Driven Blockchain-Based Authentication Scheme Azreen Shafieqah Asri; Faizatul Fitri Boestamam; Harith Zakwan Bin Zakaria; Mohammad Amir Alam Rahim Omar; Mohammad Hamka Izzuddin Bin Mohamad Yahya; Muhammad Faisal
International Journal of Computer Technology and Science Vol. 1 No. 3 (2024): July : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i3.19

Abstract

With the rapid expansion of the Industrial Internet of Things (IIoT), integrating devices, machines, and systems to optimize operations and enable data-driven decision-making, ensuring robust security measures is essential. While blockchain has shown the potential to upgrade traditional authentication methods in IIoT environments, vulnerabilities persist. This paper introduces two innovative methods to enhance blockchain-based authentication in IIoT: first, integrating AI-driven anomaly and threat detection into the blockchain authentication scheme; second, implementing Ethereum smart contracts for enhanced authentication with a two-factor authentication (2FA) system and GFE algorithms. By combining AI for anomaly detection with decentralized smart contracts and blockchain-based 2FA, and leveraging GFE algorithms to enhance blockchain capabilities, the proposed scheme aims to significantly fortify security measures. This integration offers a resilient defense against evolving threats, ensuring transparency, adaptability, and heightened security in IIoT applications.
Multi-Factor Authentication Using Blockchain: Enhancing Privacy, Security and Usability Irenna Wanisha; James, Jaymaxcklien Bravyain; Witeno, Jeremy Silas; Mohammad Bakery, Luqmanul Hakim; Samuel, Melvianna; Muhammad Faisal
International Journal of Computer Technology and Science Vol. 1 No. 3 (2024): July : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i3.24

Abstract

In the ever-changing digital world, strong security protocols are essential. As a vital line of defence against unwanted access, blockchain uses several verification techniques to boost security. This article investigates the use of blockchain technology to tackle privacy, security, and usability issues. By reducing the dangers associated with conventional centralised systems, blockchain's decentralised and immutable structure offers a secure platform for storing and verifying authentication credentials. This method increases user trust by using smart contracts to guarantee transparent and unchangeable authentication procedures. The suggested blockchain-based method strengthens security and enhances privacy by removing sources of failure and decreasing dependence on outside verification. Furthermore, user-centric design and expedited procedures improve the system's usability by making secure authentication more approachable and less obtrusive. This paper offers a thorough examination of the suggested system, stressing its benefits, possible drawbacks, and directions for future investigation. The results indicate that blockchain technology presents a viable solution to ensure that digital authentication frameworks combine privacy, security, and usability.
Enhancing Authentication Security: Analyzing Time-Based One-Time Password Systems Asyura Binti Sofian; Ayu Fitri Alafiah Binti Peradus; Fidel Yong; Irvine Shearer; Nurrul Nazwa Binti Ismail; Yugendran A/L Mahendran; Muhammad Faisal
International Journal of Computer Technology and Science Vol. 1 No. 3 (2024): July : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i3.25

Abstract

This paper explores the Time-Based One-Time Password (TOTP) authentication mechanism enhanced with lightweight cryptographic algorithms, presenting it as an advanced solution to the limitations of traditional OTP systems. There are a lot of applications and systems where this mechanism is applied. For example, bank applications, e-commerce websites, access control system, healthcare system, etc. TOTP generates dynamic, time-sensitive passwords using the current time and a secret key processed through a cryptographic hash function, significantly improving security by reducing vulnerabilities to code reused and interception. The adoption of lightweight algorithms ensures that TOTP can be efficiently implemented on resource-constrained devices, such as those on the Internet of Things (IoT) ecosystem. Despite its benefits, TOTP faces challenges including synchronization issues between client devices and servers, and a trade-off between computational efficiency and security strength. This paper discusses the implications of these challenges and evaluates how TOTP, with appropriate design considerations, can provide a robust, secure, and efficient authentication method suitable for various applications, from digital banking to IoT environments.
Advancements in Multi-Factor Authentication: A Quantum-Resilient and Federated Approach for Enhanced Security Nur Syahrina Binti Juni; Grasila Huney Wan; Siti Aisyah Nabilah Binti Banchi; Estella Blessings; Venetha A/P Loganathan; Muhammad Faisal
International Journal of Computer Technology and Science Vol. 1 No. 3 (2024): July : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i3.26

Abstract

The Internet of Things (IoT) phenomenon is centered around linking various devices and objects to the Internet, enabling them to communicate, collect, and exchange data [1]. The IoT needs strong, lightweight, and secure authorization schemes to regulate many devices with varying levels of ability. Quantum-resilient federated Multi-Factor Authentication (QRF-MFA) is a solution presented in this paper to address the above-discussed issues. Featuring quantum-resistant cryptographic protocols, high-speed and low-energy Physically Unclonable Functions (PUFs), decentralized identity management, and optimized communication protocols, QRF-MFA provides a complete solution for secure cross-domain device identification and authentication. This is done by leveraging blockchain technology for immutable and transparent management of identities yet limiting on-chain storage overhead. It also provides secure, lightweight communication well-suited for resource constrained IIoT devices, and it is designed for fog and edge computing environments as well. QRF-MFA eliminates the challenges of current methods by combining security, efficiency, and scalability and delivering a resilient and future-ready solution to secure IIoT authentication.  
Enhancing Security in Industrial IoT: Authentication Solutions Leveraging Blockchain Technology Rebecca Ling Ze Siew; Brendan Chan Kah Le; Lee Kai Yue; Nuri Nazirah Binti Ismail; Xavier Liong Zhi Hao; Muhammad Faisal
International Journal of Computer Technology and Science Vol. 1 No. 4 (2024): October: International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i3.29

Abstract

The rapid advancement of Industrial Internet of Things (IIoT) technology necessitates robust authentication solutions to ensure security, scalability, and efficiency. This project, titled "Enhancing Security in Industrial IoT: Authentication Solutions Leveraging Blockchain," examines various blockchain-based authentication methods for IIoT and identifies their strengths and weaknesses. Despite the enhanced security and decentralized nature of blockchain, issues such as scalability, high latency, and computational load persist. To address these challenges, we propose the integration of Multi-Factor Authentication (MFA) as a supplementary solution. MFA can distribute the authentication load, enhance flexibility and security, and reduce latency by utilizing quick-to-verify factors. Moreover, MFA ensures high availability and scalable storage and processing through cloud services, seamlessly integrating with existing systems to provide a superior user experience. This comprehensive approach not only mitigates the inherent limitations of blockchain technology in IIoT but also reinforces the overall security framework, ensuring resilient and efficient authentication mechanisms. The results demonstrate significant improvements in system performance and user satisfaction, establishing MFA as a viable enhancement to blockchain-based IIoT security solutions.
Preparation of Information Technology (IT) Master Plan of SMK Ma’arif 1 Metro Using the Open Group Architecture Framework (TOGAF) Method Risa Ayuna; Sutedi Sutedi
International Journal of Computer Technology and Science Vol. 1 No. 4 (2024): October: International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i4.47

Abstract

This study aims to compile an Information Technology (IT) Master Plan for SMK Ma'arif 1 Metro using The Open Group Architecture Framework (TOGAF) method. This method is applied to design a structured and integrated information technology infrastructure to support operations and learning in schools. The results of the study indicate that the main business processes in schools, such as student registration, curriculum management, and financial administration, are still carried out manually, making them prone to errors and inefficiencies. Through TOGAF, an information system architecture is designed that includes the development of the Academic Information System (SIAKAD) and other school management applications. In addition, improving network infrastructure and cybersecurity are priorities in the proposed technology architecture. Implementation strategies and change management are also formulated to ensure the success of the digital transformation. This IT Master Plan is expected to improve the efficiency of school operations and the quality of learning, as well as support the achievement of more optimal educational goals in the digital era.
Optimization Of Big Data Processing Using Distributed Computing In Cloud Environments Rahul Dev Singh; Vikram Kumar Gupta; Priya Anjali Patel
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.58

Abstract

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.
Sentiment Analysis Of Social Media Data Using Deep Learning Techniques Salsabila Septiani; Nabila Putri; Dara Jessica; Arya Saputra
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.59

Abstract

The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.
Enhancing Cybersecurity In Smart Cities Through IoT Device Management Siti Aminah Binti Ismail; Ahmad Faizal Bin Mohd Ali
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.62

Abstract

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.

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