Ransomware and Artificial Intelligence: A Comprehensive Systematic Review of Reviews

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 PRISMA, this paper gathers insights on how AI is transforming ransomware detection, prevention, and mitigation strategies during the past five years (2020-2024). The findings highlight the effectiveness of hybrid models that 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 to address the increasing complexity of ransomware. In addition, it examines key challenges in ransomware defense, including techniques designed to deceive AI-driven detection systems and the lack of strong and diverse datasets. The results highlight the role of AI in early detection and real-time response systems, improving scalability and resilience. Using a systematic review-of-reviews approach, this study consolidates insights from multiple review articles, identifies effective AI models, and bridges theory with practice to support collaboration among academia, industry, and policymakers. Future research directions and practical recommendations for cybersecurity practitioners are also discussed. Finally, this paper proposes a roadmap for advancing AI-driven countermeasures to protect critical systems and infrastructures against evolving ransomware threats.

Comparison of 5G Performance Post-Merger between Two Network Operators Using Field Tests in Urban Areas

In late Q1/2023, DTAC and TRUE officially completed their merger. Consequently, this study was initiated to ascertain whether their respective 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-values < 0.05). Remarkably, roaming functionalities were still operational between the two networks.

5G Quality of Service in Bangkok and Metropolitan Areas: Revisiting BTS Skytrain Station Areas

This article compares two of the leading mobile network operators in Thailand's telecom market in terms of the service quality of Thailand's 5G networks. The following three factors, download speed, upload speed and latency, which are frequently considered to be indicators of the quality of Internet networks, were examined. The researchers employed the test results to determine an average grade of service that was reached by comparing newly collected data to data that had previously been examined utilizing the same format and application in the middle of May 2021. The typical upload speed dropped from 62.6 Mbps in 2021 to 52.0 Mbps in 2023, while the latency increased from 14.9 to 23.3 milliseconds on average. It was established that the results delivered considerably enhanced quality values despite the fact that the test region in this study only comprised BTS stations. Furthermore, this was the case despite the fact that the test area in this study only encompassed a small percentage of the total population.