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Cyber Security and Network Management
Published by Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia
ISSN : -     EISSN : 31240054     DOI : 10.66472
Core Subject :
Aims This journal focuses on research and practical innovations in cybersecurity and network management to protect digital infrastructures and ensure secure, resilient, and reliable networked systems. Scope Network security and cyber defense mechanisms Intrusion detection and prevention systems Cryptography and blockchain security Cloud, IoT, and edge security Network monitoring and performance management Cyber risk assessment and governance Digital forensics and incident response
Arjuna Subject : -
Articles 9 Documents
 1 
Design and Evaluation of an Adaptive Intrusion Detection Framework for IoT Edge Networks Using Hybrid Machine Learning and Deep Reinforcement Learning Techniques Victor Marudut Mulia Siregar; Munji Hanafi
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.8

Abstract

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.
Risk Aware Cybersecurity Governance Model with Real Time Threat Intelligence Integration and Predictive Anomaly Detection for Enterprise Network Infrastructures Firman Pratama; Fandan Dwi Nugroho Wicaksono
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.10

Abstract

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.
A Comprehensive Study on Blockchain Based Cryptographic Key Management and Secure Communication Protocols for Large Scale Cyber Physical Systems in Industrial Environments Rudolf Sinaga; Lely Priska D Tampubolon
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.12

Abstract

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.
Secure Cloud Native Microservices Architecture with Zero Trust Network Access Controls and Multi Layered Encryption for Resilient Distributed Systems Lukman Medriavin Silalahi; Imelda Uli Vistalina Simanjuntak; Hayadi Hamuda; Irfan Kampono; Agus Dendi Rochendi; Abdul Hamid
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.14

Abstract

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.
Digital Forensics and Automated Incident Response Framework Leveraging Big Data Analytics and Real Time Network Traffic Profiling in Heterogeneous Cyber Environments Danang Danang; Zaenal Mustofa; Irlon Irlon
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.15

Abstract

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.
A Social Engineering Mitigation Model Based on Digital Security Literacy for Social Media Users in Indonesia Diana Novita; Hanifah Hanifah; Agus Herwanto
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.344

Abstract

The development of digital technology and the increasing use of social media in Indonesia have expanded people's communication activities, but have also increased the risk of security attacks based on human manipulation or social engineering. These attacks exploit user behavioral weaknesses rather than technical system vulnerabilities, thus posing a significant threat in the modern information security ecosystem. This study aims to develop a social engineering mitigation model based on digital security literacy to increase social media user awareness. The research method uses a quantitative approach with a survey technique of 210 respondents who are active social media users in Indonesia. Data were analyzed using Structural Equation Modeling–Partial Least Square (SEM-PLS) to examine the relationship between digital security literacy, cybersecurity awareness, and user vulnerability to social engineering attacks. The results show that digital security literacy significantly increases user awareness (β = 0.71; p < 0.001) and can reduce attack vulnerability by 64%. This finding emphasizes the importance of a human-centric cybersecurity approach that places humans as the primary layer of defense in digital security. The proposed mitigation model includes continuous digital security education, increased security awareness, and the implementation of adaptive authentication as a preventive strategy against social engineering attacks. This research provides practical contributions to the development of user behavior-based information security strategies and provides recommendations for educational institutions, organizations, and policymakers in strengthening the digital security resilience of Indonesian society..
Systematic Literature Review (SLR) On Consensus Mechanism In Cyber-Physical System (CPS) For Smart Farm Performance Optimization) Sri Titi Handayani; Zainal Arifin Hasibuan; Sri Supadmi
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.393

Abstract

This study aims to analyze the consensus mechanism on the Cyber-Physical System (CPS) to optimize smart farm performance through the Systematic Literature Review (SLR) approach. CPS integration is a fundamental component in smart farms as it allows real-time coordination of sensors, actuators, and computing devices to produce accurate and adaptive farming decisions. However, a dynamic and heterogeneous farming environment demands an efficient, stable, and energy-efficient consensus mechanism so that all nodes in the network can reach a consistent agreement on data or actions. Through SLR on 30 studies, this study found that the consensus mechanism was able to increase sensor synchronization by up to 30%, reduce latency by 27%, decrease water consumption by 19%, increase sensor response by 31%, and improve data security by up to 22%. Several consensus approaches such as average consensus, multi-sensor consensus, secure consensus, edge-based consensus, and fault-tolerant consensus have been proven to improve the accuracy of environmental monitoring, accelerate automatic irrigation response, optimize precision fertilization, and strengthen information search in unstable signal conditions. In addition, consensus in CPS shows a significant role in handling smart farm big data as well as strengthening system resilience to disruptions. However, this study also identified a research gap related to the need for a consensus model that is lighter, adaptive, and in accordance with the characteristics of tropical agriculture such as in Indonesia. These SLR findings provide a direction for the development of more efficient, secure, and sustainable consensus-based CPS for future smart farm implementation.
Machine Learning Model Development for Adaptive Recruitment Recommendation System Based on Portfolio Analysis and Professional Network Rizki Adha; Zainal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.410

Abstract

The rapid advancement of digital transformation and artificial intelligence has significantly reshaped recruitment processes within organizations. Conventional recruitment systems predominantly rely on curriculum vitae screening and keyword-based matching, which often fail to capture contextual competencies and relational professional evidence. This study proposes the development of an adaptive machine learning–based recruitment recommendation system that integrates professional portfolio analytics and professional network structures within a unified graphbased framework. The proposed approach adopts a Research and Development (R&D) methodology under a data-driven system development paradigm. Candidate data from an existing recruitment system are integrated with external professional data sources, including GitHub and LinkedIn. A heterogeneous graph representation is constructed to model relationships among candidates, skills, projects, and organizations. Graph Neural Networks (GNN) are employed to learn contextual relational embeddings, while a Gradient Boosting Machine (GBM) is utilized for candidate job suitability classification. The proposed framework is designed to enhance objectivity, contextual awareness, and adaptability in recruitment decision-making. By leveraging multi-source digital professional evidence and incorporating an adaptive learning mechanism, the system aims to reduce skills mismatch and improve alignment between candidate competencies and evolving industry requirements. Future work will focus on empirical validation using real-world recruitment datasets and the integration of fairness-aware and explainable AI mechanisms to ensure transparency and ethical compliance.
Predictive decision support for underutilization risk in public sector tourism: Evidence mapping and a design science roadmap Ucu Nugraha; Zainal Arifin Hasibuan; Bobi Kurniawan S; Sri Supatmi; Agus Nursikuwagus; Citra Noviyasari
Cyber Security and Network Management Vol. 1 No. 2 (2026): May: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i2.440

Abstract

Publicly funded tourism assets can become stranded when utilization persistently falls below a reasonable level relative to capacity or policy-defined potential. Yet tourism analytics research largely forecasts demand or composite performance and seldom formalizes underutilization as a governance outcome, nor evaluates decision quality within planning and budgeting workflows. This study (i) maps recent evidence and research gaps and (ii) proposes a conceptual artefact in the form of a policy-ready methodology and roadmap for developing a predictive decision support system (DSS) to mitigate underutilization risk. An evidence-mapping review of 117 Scopus-indexed studies (2021–2026) reveals a critical gap: 0% of the analyzed studies explicitly formalize "underutilization" as a policy outcome in their titles. Furthermore, evaluation procedures remain opaque, with 79.5% of studies failing to clearly specify their methodologies. In response, we outline a design-science roadmap for an auditable predictive DSS that operationalizes underutilization through two complementary metrics: the Underutilization Gap (UG) and the Utilization Ratio (UR). The proposed architecture integrates heterogeneous tourism, spatial, and socio economic data while providing traceable audit trails via Explainable AI (XAI) to ensure scores are logically defensible in public budgeting. Crucially, the framework introduces a two-layer evaluation that couples technical predictive performance (E1) with decision-utility metrics (E2), such as rank agreement and allocation efficiency. This methodology equips local governments with a practical, theoretically grounded instrument to justify prioritization, optimize resource allocation, and reduce the likelihood of underutilization-related policy failure.

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