Pomsathit, Auttapon

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Pomsathit, Auttapon
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Pomsathit, A.
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auttapon.po@kmitl.ac.th

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    Evaluation of Machine Learning Techniques for Denial-of-Service Attack Detection in Digital Information Exchange
    (2026-01-01)
    Suwimol, Piyapol
    ;
    Chimmanee, Krishna
    ;
    Pomsathit, Auttapon
    Anomaly detection plays a critical role in mitigating cybersecurity threats, particularly Distributed Denial of Service (DDoS) attacks. This study evaluates the performance of tree-based and ensemble learning models, including Decision Tree, Random Forest, and XGBoost, for classifying Snort log data, alongside the application of Isolation Forest for time-series anomaly detection. The experiments were conducted using ICMP-based Ping Flood attacks in a controlled network environment, with data collected from Snort intrusion detection system logs. The classification results indicate that XGBoost achieved the highest performance, with 99.81% accuracy, 99.93% precision, 99.65% recall, and 99.79% F1-score under a 70–30 train-test split. Random Forest and Decision Tree also demonstrated strong performance, while Logistic Regression showed lower effectiveness due to its limitations in modeling nonlinear patterns. For anomaly detection, Isolation Forest was applied to time-series data collected over a 19-day period. The model detected 93 anomaly points, of which 41 overlapped with Wireshark-confirmed events. However, a false positive rate of 41.67% was observed, indicating the need for parameter tuning to balance detection sensitivity and operational efficiency. Overall, the findings demonstrate that ensemble-based learning approaches, particularly XGBoost, are effective for detecting DDoS-related patterns within the experimental setting. However, the results are limited to ICMP-based attack scenarios in a controlled environment. Further validation, including cross-validation, multi-attack evaluation, and deployment-level performance analysis, is required to assess generalizability and practical applicability.
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    Adaptive Detection of Advanced Persistent Threats (APT) With Graph Neural Networks and Rehearsal-Based Continual Learning on Wazuh EDR Telemetry
    (2025-01-01)
    Pomsathit, Auttapon
    In the rapidly evolving cybersecurity landscape, Advanced Persistent Threats (APTs) pose major challenges due to their stealthy and adaptive behavior. Traditional detection methods based on signatures or heuristics are limited in identifying novel and evolving attacks, while static deep learning models suffer from concept drift and catastrophic forgetting, leading to degraded performance over time. This paper proposes an adaptive APT detection framework that integrates Graph Neural Networks (GNNs) with rehearsal-based continual learning using telemetry data from Wazuh, an open-source Security Information and Event Management (SIEM) and Endpoint Detection and Response (EDR) platform. Endpoint telemetry is represented as graphs where nodes denote system components and edges describe behavioral interactions among them. Experimental evaluations on real-world Wazuh telemetry augmented with sandbox-executed APT scenarios demonstrate that the proposed approach consistently achieves F1-scores above 0.98, outperforming static and fine-tuned baselines in both adaptability and knowledge retention. These results confirm that combining graph-based representations with continual learning offers a scalable, interpretable, and resilient solution for modern SOC and EDR environments facing advanced and evolving cyber threats.
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    The design of linear and circular polarization for dual band microstrip slot antenna
    (2014-01-01)
    Pomsathit, Auttapon
    ;
    Rakluea, Paitoon
    ;
    Anantrasirichai, Noppin
    ;
    Benjangkaprasert, Chawalit
    ;
    Wakabayashi, Toshio
    This paper presents a lightweight antenna for wireless communication systems. The proposed antenna has been designed to be a dual-band and dual-polarized antenna by using a right-angled slot structure fed a by microstrip line. The designed antenna is composed of three right-angled slot radiators on the ground plane. The first two radiators are right-angled slots of similar scale which are added to generate circular polarization at 4.95 GHz, while the last one has been designed for linear polarization at 2.45 GHz. Furthermore, in order to achieve dual-band operation and dual polarization with good matching, a special arrangement is proposed. The results of simulation and measurements such as return loss, axial ratio, and radiation patterns are shown at the resonant frequencies of 2.45 and 4.95 GHz. Details of the experimental results are presented and discussed. In addition, the presented antenna can operate and cover the applications of a wireless local area network (WLAN IEEE 802.11 a/b/g/j/n). © 2014 Institute of Electrical Engineers of Japan.