KMITL

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

Browse

Search Results

Now showing 1 - 10 of 38

Some of the metrics are blocked by your
consent settings
DDoS Detection Framework Using Machine Learning Optimized by Bayesian and PSO Techniques
(2026-07-01)
Sathaporn, Posathip
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Benjangkaprasert, Chawalit
;
Archevapanich, Tuanjai
This paper presents a distributed denial of service (DDoS) detection framework using machine learning techniques enhanced with hyperparameter optimization for network traffic classification and evaluated on the BCCC-cPacket-Cloud-DDoS-2024 dataset. The framework includes data preprocessing with normalization and class imbalance handling via the synthetic minority over-sampling technique. A critical contribution of this study is the rigorous analysis of the trade-off between detection accuracy and model complexity. Unlike arbitrary feature selection methods, we empirically determined the optimal feature set using information gain, identifying that the top 100 features represent the saturation point that balances high accuracy with minimal overhead. Model performance was further improved through hyperparameter optimization using particle swarm optimization and Bayesian algorithms. The extreme gradient boosting (XGBoost) model optimized using Bayesian optimization and the top 100 features achieved the highest performance, with an accuracy of 99.29% and an F1-score of 98.91%. As a result, the proposed framework improves detection performance while reducing model complexity by selecting an optimal feature set to improve model stability and efficiency.
Some of the metrics are blocked by your
consent settings
DDoS Detection Using a Hybrid CNN–RNN Model Enhanced with Multi-Head Attention for Cloud Infrastructure
(2025-11-01)
Sathaporn, Posathip
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Benjangkaprasert, Chawalit
;
Purahong, Boonchana
Cloud infrastructure supports modern services across different sectors, such as business, education, lifestyle, government and so on. With the high demand for cloud computing, the security of network communication is also an important consideration. Distributed denial-of-service (DDoS) attacks pose a significant threat. Therefore, detection and mitigation are critically important for reliable operation of cloud-based systems. Intrusion detection systems (IDS) play a vital role in detecting and preventing attacks to avoid damage to reliability. This article presents DDoS detection using a convolutional neural network (CNN) and recurrent neural network (RNN) model enhancement with a multi-head attention mechanism for cloud infrastructure protection enhances the contextual relevance and accuracy of the DDoS detection. Preprocessing techniques were applied to optimize model performance, such as information gained to identify important features, normalization, and synthetic minority oversampling technique (SMOTE) to address class imbalance issues. The results were evaluated using confusion metrics. Based on the performance indicators, our proposed method achieves an accuracy of 97.78%, precision of 98.66%, recall of 94.53%, and F1-score of 96.49%. The hybrid model with multi-head attention achieved the best results among the other deep learning models. The model parameter size was moderately lightweight at 413,057 parameters with an inference time in a cloud environment of less than 6 milliseconds, making it suitable for application to cloud infrastructure.
Some of the metrics are blocked by your
consent settings
Centralizing Data Warehouse Platform for Mining Management Using AWS Computing Instance
(2024-01-01)
Sathaporn, Posathip
;
Chaowalittawin, Vasutorn
;
Krungseanmuang, Woranidtha
;
Benjangkaprasert, Chawalit
;
Purahong, Boonchana
Mining industry is one of significant industry in the world. In order to increase efficiency, safety, sustainability and environmental impacts in mining process. This article presents a centralized data platform for mining process by design based on microservices architecture which can be supported various of input data source such as manual form, excel file and internet of things (IoT) device and notification when the value is abnormal. Our platform implementation on Amazon Web Service (AWS) cloud. For development of productivity in process and controlling quality the mine environment. The results of the platform operations with various source of data acquisition with response time is less than 1.5 seconds per each request.
Some of the metrics are blocked by your
consent settings
Phase Error Criterion Based Adaptive Algorithm for Frequency Estimation
(2024-01-01)
Inban, Prayuth
;
Punchalard, Rachu
;
Benjangkaprasert, Chawalit
A simple phase error criterion (PEC-)-based adaptive algorithm for estimating the frequency of a complex sinusoidal signal in additive white Gaussian and impulsive noises is proposed. The proposed technique makes use of the instantaneous phase response of a first-order complex linear predictor (CLP) as a driving function to update the frequency parameter of the CLP. The proposed PEC is attractive due to its simplicity and high impulsive noise robustness. Theoretical analysis for the mean value of the estimated frequency and the steady-state mean square error (MSE) of the frequency estimate are derived in closed forms. Computer simulations are drawn to show the performance of the proposed frequency estimator.
Some of the metrics are blocked by your
consent settings
Implementation of Cloud Computing and Internet of Things (IoT) by Performance Evaluation
(2024-01-01)
Sithiyopasakul, Jiran
;
Archevapanich, Tuanjai
;
Sithiyopasakul, Saran
;
Lasakul, Attasit
;
Purahong, Boonchana
The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.
Some of the metrics are blocked by your
consent settings
Performance Evaluation of Infrastructure as a Service across Cloud Service Providers
(2023-01-01)
Sithiyopasakul, Saran
;
Archevapanich, Tuanjai
;
Purahong, Boonchana
;
Sithiyopasakul, Paisan
;
Lasakul, Attasit
The purpose of this research aims to monitor, analyze, and compare the performance of infrastructure as a service (IaaS) between the selective cloud providers. To assure which cloud provider has more stability, reliability, and scalability. This paper focuses on performance testing based on a deployed web server in the cloud environment. The main feature of cloud computing is scalability thus most common IaaS cloud service providers (CSPs) have Auto Scaling features for instances or virtual machines. Not only does this paper gives the experimental results of the scaling scalability testing, but it also provides the results of recovery testing to inspect how long a web server is able to recover from failures and load testing which simulated traffic requests. Testing was conducted in the major public clouds of Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Azure performs the most efficiently of almost all testing but hardest to configure.
Some of the metrics are blocked by your
consent settings
Landing Runway Assignment by Airport Traffic using Machine Learning
(2022-05-27)
Kanjanasurat1, Isoon
;
Jungsuwadee, Wasarut
;
Purahong, Boonchana
;
Benjangkaprasert, Chawalit
This paper presents the solutions to the overwhelming burden of air traffic controllers by reducing workload and optimizing runway capacity using machine learning tools to assign runways for incoming aircraft based on critical information such as aerodrome traffic information of aircraft taking off and landing on the runway at Suvarnabhumi Airport, THAILAND. The model is composed of four layers and three hidden layers. ReLU and Adam are the activation and optimization functions used in this model, respectively. The model was trained using assigned landing runway and traffic runway factors. Predicting the assigned runway is 82.77 percent accurate.
Some of the metrics are blocked by your
consent settings
Comparison of Support Vector Machine for Apron Allocation
(2022-05-27)
Kanjanasurat1, Isoon
;
Purahong, Boonchana
;
Teerapanpong, Saowaluk
;
Benjangkaprasert, Chawalit
This paper presents machine learning techniques for classifying parking stand locations in the apron allocation management service that affects total airport ground service processing time at airports where arriving aircraft land. SVM and Kernel SVM algorithms will be used, as well as Polynomial, Gaussian RBF, and Sigmoid, based on five input factors: aircraft identification, estimated time of arrival (ETOA), area of apron, type of aircraft, and target of stands. Then, we compared classification accuracy and performance using the Mean Absolute Error (MAE) and the squared mean error (Root Mean Square Error: RMSE), and discovered that the Gaussian RBF kernel of the SVM algorithm model is more accurate than the other model. This work may be beneficial in assisting airport's decision-makers and enhancing airport operations efficiency and predictability.
Some of the metrics are blocked by your
consent settings
Malware Developing Guide: Encryption and Decryption
(2022-01-01)
Chatsomsanga, Jutiphat
;
Benjangkaprasert, Chawalit
This research aims to understand malware from malware author's view. Starting from Ransomware, we attempt to develop ransomware written in python and encrypt all files in Windows platform without doing harm to crucial system files. In this research we use Asus K550J laptop with Windows 10 education (Build 19042) as a victim machine. We ran encryption and decryption program with different privilege and observe the results as well as time takes to encrypt and decrypt files. Results show that encryption process is much slower than decryption process and both encryption and decryption work slower in the bigger file size.
Some of the metrics are blocked by your
consent settings
Voice over IP Integration Platform Performance Using EC2 AWS Cloud Service
(2022-01-01)
Sathaporn, Posathip
;
Krungseanmuang, Woranidtha
;
Chaowalittawin, Vasutorn
;
Anuwongpinit, Thanavit
;
Purahong, Boonchana
This article describes a method for integrating a mobile application for controlling and transmitting voice data levels in various departments within an organization with Amazon Elastic Compute Cloud (AWS EC2) to reduce hardware location costs and create more convenient in-house management at a single point. To begin, the paper introduces the project objective with a business scenario from an organization. Second, the SIP server implementation method is provided by Asterisk on AWS EC2 Ubuntu operating system and connection with a Mobile application that is used by flutter framework. Finally, the project experiments and discussions will be presented, and the obtained results show that the call setup time for the iOS/Android platforms to PC performed the best, taking less than one second, and was the fastest when compared to other testing metrics. However, there are many more metrics that should be considered, which are presented in the research's results section. With high performance and stability, this article was able to broadcast voice data via mobile applications.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results