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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Computer Networks, Architecture and High Performance Computing
Lukas Hadi Purnama, Lukas Hadi
Jurusan Teknik Elektro Universitas Surabaya, Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education

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Performance and Risk Assessment of Honeypots on IoT and VPS Using COBIT 2019 and Stress Test Purnama, Lukas Hadi; Daniel Hary Prasetyo
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6661

Abstract

The massive wave of digital transformation has increased the complexity of cyber threats, particularly targeting vital network services. Honeypots have emerged as an effective approach for detecting and analyzing attacks, yet platform selection and management strategies remain a challenge. This study analyzes the performance, management, and risks of two types of honeypots, Cowrie (medium interaction) and Heralding (low interaction), implemented in different computing environments, based on the COBIT 2019 framework (domains EDM03, APO12, and DSS05). Evaluation was conducted through experiments on SSH, Telnet, FTP, SMB, MySQL, and HTTP services, utilizing both isolated and multistage honeypot scenarios. The results show that both honeypot deployments effectively capture brute force and botnet attack patterns and enable accurate logging and validation of attack activities. The analysis of false positive rates and structured log validation processes produced more accurate and relevant attack data. This study is among the first to provide a holistic evaluation of Cowrie and Heralding honeypots with direct COBIT 2019 integration, presenting a novel perspective on governance-driven risk management in honeypot implementation. The application of the COBIT framework ensures that honeypot deployment is not only technically effective but also aligned with robust governance and risk management practices for information security. Strategic recommendations are provided regarding configuration optimization, platform selection, and COBIT-based governance integration to enhance organizational cybersecurity resilience
Effectiveness of Artificial Intelligence-Based Adaptive Honeypots in Cyber Threat Detection: A Systematic Literature Review and Meta-Analysis Purnama, Lukas Hadi; Prasetyo, Daniel Hary
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2403

Abstract

This study conducted a systematic literature review and meta-analysis on using honeypot systems enhanced by artificial intelligence to improve the effectiveness of cyber threat detection in organizational environments. The review followed the PRISMA protocol and assessed 454 articles from databases, including IEEE, ACM, Emerald, and Web of Science. After a multi-stage screening process, 62 articles met the inclusion criteria and were further analyzed. The synthesis indicated that integrating artificial intelligence into honeypot systems improved detection accuracy, expanded the system’s ability to recognize varied attack patterns, and optimized resource efficiency. A meta-analysis of 36 studies revealed consistent, significant improvements in detection performance. Quantitatively, the analysis yielded a mean effect size of 0.905, indicating a substantial improvement in detection effectiveness resulting from AI integration. These findings confirm that adopting AI-based honeypot technologies is essential for addressing increasingly complex cyberattacks and for providing a foundation for future research into the development of standardized evaluation frameworks.
Co-Authors Daniel Hary Prasetyo Prasetyo, Daniel Hary