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All Journal TEPIAN Green Intelligent Systems and Applications
Atanda, Abdulwahab Funsho
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Optimisation of Network Logs for Fake Bandwidth Classification using CNN Nurcahyo, Azriel Christian; Yong , Ting Huong; Atanda, Abdulwahab Funsho
TEPIAN Vol. 6 No. 2 (2025): June 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i2.3260

Abstract

The goal of this study is to enhance the classification accuracy of fake bandwidth using a CNN model, leveraging network logs collected in real-time. For this research, the network logs from the Cyber Security Laboratory of the University of Technology Sarawak are used as a dataset for training the CNN model. The dataset consists of 20 days of continuous network activity logging, which results in over 500,000 data entries. According to the model evaluation results, the trained CNN model demonstrated high accuracy in classifying genuine bandwidth (Precision: 0.92, Recall: 0.95). Moreover, it achieved considerable success in detecting fake bandwidth (Precision: 0.89, Recall: 0.90) and the no heavy activity category (Precision: 0.98, Recall: 0.84). Analysis of Loss Over Epochs showed a dramatic decrease in loss during the training phase, with optimal convergence reached by epoch 2000. Identifying these characteristics enables monitoring systems to classify network data with high certainty, detecting bandwidth manipulation in expansive networks. Thus, this research aids the design of dynamic network monitoring systems that require minimal response time while maintaining high accuracy.
Durian Species Classification Using Deep Learning Method Teo, Boon Chen; Ting, Huong Yong; Atanda, Abdulwahab Funsho
Green Intelligent Systems and Applications Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i1.352

Abstract

Durian is a popular fruit in Southeast Asia, and the market offers various species of durians. Accurate species classification is crucial for quality control, grading, and marketing. However, the complexity of this task has led to the utilization of machine learning and deep learning methods. Traditional machine learning algorithms, such as K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machines, and Random Forests, have demonstrated good accuracy, but they require extensive feature engineering. Deep learning algorithms, particularly Convolutional Neural Networks, can automatically extract features, making them less dependent on manual feature selection. This research aims to review deep learning classification algorithms, including Convolutional Neural Networks and Recurrent Neural Networks, to determine the most suitable algorithm for an efficient and accurate durian classification system. The objective is to enhance the precision and speed of durian species classification, presenting potential advantages for both durian producers and consumers. The literature review revealed that Convolutional Neural Networks outperformed other deep learning and traditional machine learning algorithms on datasets of varying sizes, achieving the highest accuracy of 98.96% through techniques like image resizing, color conversion, and additional parameters such as days harvested and dry weight. Deep learning emerges as a promising approach for robust and accurate durian species recognition, with future directions including developing models to classify durian species from different plant parts and even real-time video analysis. However, while Convolutional Neural Networks lead the way, a critical research gap exists in identifying optimal features, necessitating further investigation to refine durian species recognition accuracy.
Enhancing Supply Chain Traceability through Blockchain and IoT Integration: A Comprehensive Review Wong, Elton Kee Sheng; Ting , Huong Yong; Atanda, Abdulwahab Funsho
Green Intelligent Systems and Applications Volume 4 - Issue 1 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i1.355

Abstract

Supply chain traceability is essential for ensuring safety, preventing counterfeit goods, and improving efficiency. The integration of blockchain technology and the Internet of Things (IoT) has emerged as a transformative approach to enhance supply chain traceability by creating a secure, transparent, and efficient way to track the movement of goods and materials. This comprehensive literature review examines how the integration of blockchain and the Internet of Things can enhance supply chain traceability, utilizing a systematic literature search to identify and analyze all relevant studies. Recent and related articles selected from the Scopus database were reviewed. Our analysis underscores the potential for blockchain and IoT integration to provide end-to-end visibility, secure data sharing, and real-time monitoring across the supply chain ecosystem. It also identifies Machine Learning (ML) as another key component that enhances the security challenges of the Internet of Things while simultaneously serving as an analytical tool in Supply Chain Management (SCM). The review concludes that the integration of blockchain, the Internet of Things, and ML has the potential to transform supply chain traceability. By providing a secure, transparent, and efficient way to track the movement of goods and materials, businesses can improve their operations and offer better products and services to their customers. However, these findings do not impact the results of this research work. Additional research and a more extensive examination of the literature could offer a more comprehensive insight into the subject matter.
Classification of Simulated Fake Bandwidth Data Using LSTM Nurcahyo, Azriel Christian; Yong, Alan Ting Huong; Atanda, Abdulwahab Funsho
TEPIAN Vol. 5 No. 3 (2024): September 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i3.3106

Abstract

Hardly will someone acknowledge that the bandwidth we use every day is as authentic as most ISPs advertise, even those offering dedicated services. There are usually shortcomings, especially on upload and download bandwidth speeds. This paper presents the classification of simulated fake bandwidth data using the Long Short-Term Memory model, which though seldom found, is a very effective approach in network analysis. There were 1400 bandwidth data points collected from the MikroTik RB 1100 AHx device in a month, then further processed with normalization, and divided to have 80% training and 20% testing. The LSTM model applied had an accuracy rate of 98.93%, proving that it is capable of classifying either genuine or fake bandwidth instances accordingly. Of 1,400 test data points, the model managed to classify 723 as fake bandwidth and another 677 as genuine, resulting in a classification error rate of only 1.07%. The results clearly prove that LSTM has huge potential for real-time bandwidth manipulation detection, key to enhancing trust and efficiency in network management. In this respect, this research shows that bandwidth analysis combined with LSTM can be an original solution for network monitoring
Co-Authors Nurcahyo, Azriel Christian Teo, Boon Chen Ting , Huong Yong Ting, Huong Yong Wong, Elton Kee Sheng Yong , Ting Huong Yong, Alan Ting Huong