Code- Title: 1a – Implementation and testing of a research platform in Indonesia for “A 100G Dynamic Network Testbed

Lead: ITB – Eueung Mulyana, Galih Nugraha Nurkahfi

This research aims to implement a high-speed and dynamic network testbed, which will serve as the foundation for the work in CBR1b and1c.

Code- Title: 1b-IEEE802.11bd-based software-defined vehicular networking real environment testing

Lead: ITB – Eueung Mulyana, Galih Nugraha Nurkahfi

This research aims to implement an IEEE 802.11bd SDVN software testbed, which will serve as the foundation for the SDN based TAS and TPA algorithms to overcome the blocking issue in the dense-VANET area.

Code- Title: 1c-DDoS Attack Detection Framework using Machine Learning in Software Defined Network

Lead: USM- Mohd Najwadi Yusoff, Yung-Wey Chong

This research aims to develop a robust machine learning-based DDoS attack detection framework for SDN networks, utilizing the data plane and the availability of a P4 programmable data plane to offload feature extraction.

Code- Title: 2-Implementation and experiment of the research platform in South Sulawesi Province, Indonesia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier”

Lead: UNHAS-Muhammad Niswar

This research aims to develop an application to identify various types of TLS-basedcyber attacks on servers in the encrypted network using machine learning classifiers.

Code- Title: 2-Implementation and experiment of the research platform in East Java Province, Indonesia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier”

Lead: IUB-Achmad Basuki

This research aims to develop an application to identify various types of TLS-basedcyber attacks on servers in the encrypted network using machine learning classifiers.

Code- Title: 2-Implementation and experiment of the research platform in Aceh Province, Indonesia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier”

Lead: USK- Rahmad Dawood

This research aims to develop an application to identify various types of TLS-based cyber attacks on servers in the encrypted network using machine learning classifiers.

Code- Title: This research aims to develop an application to identify various types of TLS-basedcyber attacks on servers in the encrypted network using machine learning classifiers.

Lead: USM- Shankar Karuppayah, Yung-Wey Chong

This research aims to develop an application to identify various types of TLS-basedcyber attacks on servers in the encrypted network using machine learning classifiers.

Code- Title: 2-Implementation and experiment of the research platform in Dhaka, Bangladesh for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier”

Lead: BUET- Hossen Asiful Mustafa, Md. Jarez Miah

This research aims to develop an application to identify various types of TLS-basedcyber attacks on servers in the encrypted network using machine learning classifiers.

Code- Title: 3b-Research Platform
Implementation and experiment of the research platform in Banda Aceh, Indonesia for “Artificial Intelligence of Things System to Classify and Predict the Quality of Produce in Smart Agriculture”

Lead: USK- Rahmad Dawood, Maya Fitria, Masduki Khamdan

This research aims to design and develop an artificial intelligence of things (AIoT) system for smart agriculture, collect produce data in Banda Aceh, Indonesia, and determine specific classifications for selected produce. Additionally, it seeks to create a visual-based system and learning model capable of performing tasks similar to manual identification and grouping, and to design a sorting machine that incorporates this visual-based system to categorize produce by quality and category.

Code- Title: 3b-Research Platform Implementation and experiment of the research platform in East Java, Indonesia for “Artificial Intelligence of Things System to Classify and Predict the Quality of Produce in Smart Agriculture”

Lead: UB-Raden Arief Setyawan, Muhammad Aziz Muslim, Achmad Basuki, Rizal Setya Perdana

This research aims to design and develop an artificial intelligence of things (AIoT) system for smart agriculture, collect produce data in East Java, Indonesia, and determine specific classifications for selected produce. Additionally, it seeks to create a visual-based system and learning model capable of performing tasks similar to manual identification and grouping, and to design a sorting machine that incorporates this visual-based system to categorize produce by quality and category.

Code- Title: 3b- Specification of Research Platform Implementation and experiment of the research platform in Makassar, Indonesia for “Artificial Intelligence of Things System to Classify and Predict the Quality of Produce in Smart Agriculture”

Lead: UNHAS-Muhammad Niswar

This research aims to design and develop an artificial intelligence of things (AIoT) system for smart agriculture, collect produce data in Makassar, Indonesia, and determine specific classifications for selected produce. Additionally, it seeks to create a visual-based system and learning model capable of performing tasks similar to manual identification and grouping, and to design a sorting machine that incorporates this visual-based system to categorize produce by quality and category.

Code- Title: 3b- Specification of Research Platform Implementation and experiment of the research platform in Malaysia for “Artificial Intelligence of Things System to Classify and Predict the Quality of Produce in Smart Agriculture”

Lead: UNHAS-Muhammad Niswar

This research aims to design and develop an artificial intelligence of things (AIoT) system for smart agriculture, collect produce data in Malaysia and determine specific classifications for selected produce. Additionally, it seeks to create a visual-based system and learning model capable of performing tasks similar to manual identification and grouping, and to design a sorting machine that incorporates this visual-based system to categorize produce by quality and category.

Code- Title: 3c- Implementation and experiment of the research platform
in Timor-Leste for “Community Water Resource Monitoring using IoT”

Lead:

• UNTL-Domingos de Sousa Freitas
• USK-Rahmad Dawood
• USM-Yung-Wey Chong

This research aims to empower local communities in Timor-Leste by designing and implementing a smart water monitoring system utilizing IoT technology.