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All Journal PROSIDING SEMINAR NASIONAL Autotech: Jurnal Pendidikan Teknik Otomotif Universitas Muhammadiyah Purworejo E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Elkom: Jurnal Elektronika dan Komputer PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MANAJEMEN Jurnal Universal Technic Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Riset Rumpun Seni, Desain dan Media (JURRSENDEM) Jurnal Sains dan Ilmu Terapan Jurnal Sistem Informasi dan Ilmu Komputer Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Elektronika dan Teknik Informatika Terapan Journal of Technology Informatics and Engineering Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Penelitian Teknologi Informasi dan Sains Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi International Journal of Computer Technology and Science International Journal of Information Engineering and Science International Journal of Electrical Engineering, Mathematics and Computer Science Jurnal Bengawan Solo: Pusat Kajian Penelitian dan Pengembangan Daerah Kota Surakarta Systematic Literature Review Journal Journal of New Trends in Sciences Router : Jurnal Teknik Informatika dan Terapan Journal of Engineering, Electrical and Informatics Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Publikasi Ilmu Komputer dan Multimedia Jurnal Publikasi Teknik Informatika Sinov : Media Informasi Penelitian Kabupaten Semarang
Danang Danang
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Journal : international journal of computer technology and science

 1 
Systematic Literature Review on the Application of Blockchain in Enhancing Server Security: Research Methods for Mitigating Ransomware and Malware Attacks Danang Danang; Maya Utami Dewi; Widya Aryani
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.v1i4.186

Abstract

This study aims to explore the application of blockchain in enhancing server security to mitigate ransomware and malware attacks in critical infrastructures such as healthcare, finance, and government sectors. Using a systematic literature review (SLR) approach, the research collects articles from four major databases (IEEE Xplore, Scopus, ScienceDirect, and SpringerLink) published between 2020 and 2024. The search focuses on keywords related to blockchain, server security, ransomware, malware, and attack mitigation. The results indicate that blockchain enhances data integrity, transaction security, and strengthens access control to protect sensitive data. Moreover, integrating blockchain with intrusion detection systems (IDS) and using smart contracts accelerates threat detection and response, allowing for automatic blocking and data recovery from attacks. This technology reduces reliance on manual intervention and increases operational efficiency. However, the main challenges in its implementation include high implementation costs, scalability, and technical complexity. Nevertheless, blockchain offers significant solutions for mitigating ransomware and malware attacks while enhancing the reliability and efficiency of systems. In conclusion, blockchain provides an effective solution for server security and cyber threat mitigation, although challenges related to cost and scalability need to be addressed. Further research is required to develop more efficient blockchain protocols and integrate them with other technologies to enhance threat detection and response speed.
Hybrid Zero Trust Container Based Model for Proactive Service Continuity under Intelligent DDoS Attacks in Cloud Environment Danang Danang; Eko Siswanto; Nuris Dwi Setiawan; Priyo Wibowo
International Journal of Computer Technology and Science Vol. 2 No. 3 (2025): 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.v2i3.291

Abstract

Growth rapid computing cloud, especially on academic, government, and service platforms. public, has trigger improvement frequency and complexity Distributed Denial of Service (DDoS) attacks. Intelligent DDoS attacks AI based capable copy pattern Then cross user valid, so that difficult detected and mitigated. The majority approach mitigation moment This nature reactive, no scalable, and tends to sacrifice availability service for authorized users. Research​ This aiming develop architecture proactive and adaptive defense​ For ensure continuity service during attack ongoing. Security model proposed hybrid​ integrating Zero Trust Architecture (ZTA), adaptive bandwidth control, and isolation service container -based. Architecture consists of from three layer Main: (1) ZTA Policy Engine which performs verification identity and assessment behavior through tokens and policies intelligent; (2) Adaptive Bandwidth Load Balancer which automatically dynamic separate and arrange Then cross based on reputation and level trust ; and (3) Containerized Service Cluster which groups request to in different containers For user trusted and not known . Components addition such as blockchain -based smart contracts are used For recording request and verification access , as well as lightweight AI module used for profiling then cross in real-time. Simulation results show that this model succeed increase availability service for user trusted during attack , press false positive rate , as well as optimize allocation source power. Integration of zero trust policies with intelligence Then cross and segmentation service in real-time forming framework effective and scalable defense​ to modern DDoS threats . In conclusion , the study This contributes a robust , adaptive , and modular architectural model for maintain continuity cloud services in condition network at risk .
Evaluating Trust Aware Machine Learning Models for Secure Data Sharing in Distributed Internet of Things and Edge Computing Infrastructures Eko Siswanto; Danang Danang; Sunarmi Sunarmi
International Journal of Computer Technology and Science Vol. 1 No. 1 (2024): 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.v1i1.359

Abstract

The rapid growth of Internet of Things (IoT) and edge computing technologies has introduced new security challenges due to the distributed, heterogeneous, and dynamic nature of these environments. Conventional static security mechanisms, such as rulebased authentication and fixed trust models, are often inadequate for addressing evolving threats and abnormal behaviors in largescale IoT systems. To overcome these limitations, this study proposes a machine learningbased trust evaluation framework for enhancing security in distributed IoT environments. The proposed approach dynamically assesses the trustworthiness of IoT nodes by analyzing behavioral and interactionbased features collected at the edge layer. Machine learning models are trained to classify nodes into trusted and malicious categories and continuously update trust values in response to changing network conditions. Based on the predicted trust levels, adaptive security decisions are enforced to allow or restrict node participation in data sharing and computation processes. A quantitative experimental evaluation is conducted using simulated distributed IoT scenarios that include both normal and malicious behaviors. The performance of the proposed framework is evaluated using standard metrics such as accuracy, precision, recall, F1score, and detection effectiveness, and is compared against conventional static trust and rulebased security mechanisms. The results demonstrate that the proposed machine learningbased trust evaluation approach achieves significantly higher detection accuracy and robustness while maintaining low computational overhead. Overall, the findings confirm that integrating machine learning into trust management provides an effective and scalable solution for securing distributed IoT systems under dynamic and adversarial conditions.
Co-Authors Agus Priyadi Agustinus Budi Santoso Ahmad Ashifuddin Aqham Ahmad Zainudin Aldo Raditya Pangestu Andik Prakasa Hadi Arie Atwa Magriyanti Budi Santoso Dani Sasmoko Daniel Rudjiono Dendy Kurniawan Dewi Retno Putri Eka Satria Wibawa Ekky Fredyan Eko Siswanto Eni Muhadi Fa`iq Khotibul Umam Febryantahanuji Febryantahanuji Greget Widhiati Hanif Assabib Rosyid Hendri Rasminto Iman Saufik Suasana Indra Ava Dianta Katon Abdul Fatah Kenny Setiawan Khoirur Rozikin Kusumajaya, Robby Andika Laily Eka Andrianni Lilis Suryani Mat Toyib Maya Utami Dewi Muhamad Khoirul Umam Muhammad Ainun Najib Mukhammad Ibnu Khalim Neilin Nikhlis Nuris Dwi Setiawan PADJAR SETYO BUDI Priyadi Priyadi Priyo Wibowo Puteri Anindya Maulan Raden Mohamad Herdian Bhakti Rafael Ardian Fahrezi Rizky Ni'ma Febriani Siswanto Siswanto Siswanto Siswanto Siswanto Siswanto Siswanto, Edy Siti Kholifah Sulartopo Sulartopo Sunarmi Suprapti Suprapti Suwardi Suwardi Tanveer Shah Teguh Setiadi Toni Wijanarko Adi Putra Uswatul Chasanah Widya Aryani Wijaya Heti Susanti Zaenal Mustofa Zellius Ragiliawan