KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Chimmanee, Krishna

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Denial of Service Attack Detection in Digital Information Exchange by Using ML Techniques
    (2024-01-01)
    Suwimol, Piyapol
    ;
    Pomsathit, Auttapon
    ;
    Chimmanee, Krishna
    Denial of Service attack detection plays a significant role in the field of digital information exchange security, and log messages recording detailed system runtime information has become an important data analysis object accordingly. To improve traditional detection technology, several anomaly detection mechanisms, particularly the machine learning method, have been presented in recent years. This research proposes a technique for detecting anomalies in web log files that uses two machine learning algorithms. Isolation Forest is used to generate a set of features targeting traditional, while XGBoost is a tree-based model used for classification. The experimental data comes from the real web server for digital information exchange environment where log files have been collected, which contain many true intrusion messages. After comparing with two types of machine learning algorithms used in anomaly detection, testing results for this data set indicate that this system has a greater detection accuracy and can detect unknown anomaly data.