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All Journal Bulletin of Electrical Engineering and Informatics
Kritphon Phanrattanachai
Phetchabun Rajabhat University
Electrical & Electronics Engineering

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Ransomware and artificial intelligence: a comprehensive systematic review of reviews Therdpong Daengsi; Phisit Pornpongtechavanich; Paradorn Boonpoor; Kathawut Wattanachukul; Korn Puangnak; Kritphon Phanrattanachai; Pongpisit Wuttidittachotti; Paramate Horkaew
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11107

Abstract

This study provides a comprehensive synthesis of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) in ransomware defense. Using a “review of reviews” methodology based on the PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies in the past five years (2020-2024). The findings highlight the effectiveness of hybrid models, which combine multiple analysis techniques such as code inspection (static analysis) and behavior monitoring during execution (dynamic analysis). The study also explores anomaly detection and early warning mechanisms before encryption that tackle ransomware’s growing complexity. It also examines key challenges in ransomware defense, such as techniques designed to deceive AI driven detection, and the lack of strong and diverse datasets. It highlights AI’s role in early detection and real-time response systems, enhancing scalability and resilience. With the systematic review of reviews approach, the contributions of this study are systematically consolidating research insights from multiple review articles, identifying effective AI models, and bridging theory with practice to foster collaboration among academia, industry, and policymakers. Future research directions are anticipated and practical recommendations for cybersecurity practitioners are provided. Finally, it presents a roadmap for advancing AI-driven countermeasures, for the protection of key systems and infrastructures against evolving ransomware threats.
Comparison of 5G performance post-merger between two network operators using field tests in urban areas Surachai Chatchalermpun; Therdpong Daengsi; Pakkasit Sriamorntrakul; Kritphon Phanrattanachai
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8307

Abstract

In late Q1/2023, DTAC and TRUE officially completed their merger. Consequently, this study was initiated to ascertain whether their respective fifth-generation (5G) networks had been seamlessly integrated several months following the merger. The investigation involved conducting drive tests along two predefined routes within the urban areas of Bangkok, employing the G-NetTrack pro tool for testing and data collection. Additionally, stationary tests were conducted in two crowded places using an application called Speedtest. Subsequently, an array of quality of service (QoS) metrics, including reference signal received power (RSRP), reference signal received quality (RSRQ), signal to noise ratio (SNR), download (DL), upload (UL) speeds and latency, were meticulously analyzed and presented. The findings of this study unveiled that, despite the successful completion of the DTAC and TRUE merger from a business standpoint, the technical integration of their respective 5G networks had not been finalized, although there were no significant differences between DTAC and TRUE for DL (p-value=0.542) and UL (p-value=0.090). Notably, significant differences were found between DTAC and TRUE for four metrics, including RSRP, RSRQ, SNR and latency (p-values0.05). Remarkably, roaming functionalities were still operational between the two networks.
Co-Authors Kathawut Wattanachukul Korn Puangnak Pakkasit Sriamorntrakul Paradorn Boonpoor Paramate Horkaew Phisit Pornpongtechavanich Pongpisit Wuttidittachotti Surachai Chatchalermpun Therdpong Daengsi