CBR is a scheme for promoting multi-stakeholder collaborative research and inclusive support.
A CBR project group formed by teams from member universities will collaborate and engage on an agreed-upon research topic. To facilitate CBR activities, AI3 /SOI Asia project will provide support such as funding, project management, education and outreach of the results.
Joint research inside the community has been one of the strengths of the SOI Asia and AI3 community. This CBR scheme is expected to lower the hurdles for starting joint research between universities, catalyze and ignite activities. In addition, mutual research guidance will enable better researchers joint training. We aim to improve the level of research in the Asia-Pacific and contribute to the region through research outcomes.
CBRs approved by AI3 /SOI Asia
| Code- Title | Lead | Description |
| 1a- Implementation and testing of a research platform in Indonesia for “A 100G Dynamic Network Testbed | 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. |
| 1b- IEEE802.11bd-based software-defined vehicular networking real environment testing | 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. |
| 1c- DDoS Attack Detection Framework using Machine Learning in Software Defined Network | 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 | Lead | Description |
| 2- Implementation and experiment of the research platform in South Sulawesi Province, Indonesia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier” | 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. |
| 2- Implementation and experiment of the research platform in East Java Province, Indonesia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier” | UB- 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. |
| 2- Implementation and experiment of the research platform in Aceh Province, Indonesia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier” | USK- Rahmad Dawood | 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. |
| 2- Implementation and experiment of the research platform in Malaysia for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier” | 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. |
| 2- Implementation and experiment of the research platform in Dhaka, Bangladesh for “Real-time Malicious TLS Traffic Detection using Machine Learning Classifier” | 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 | Lead | Description |
| 3a- Implementation and experiment of the research platform in Malaysia for “An IoT-based transport data collection and analytics framework using Bluetooth proximity beacons | USM- Yung-Wey Chong, Sye-Loong Keoh | This research aims to propose a low-cost, efficient, and innovative IoT solution to track the location of buses without requiring the deployment of a GPS device in Penang, Malaysia. |
| 3a- Implementation and experiment of the research platform in East Java for “An IoT-based transport data collection and analytics framework using Bluetooth proximity beacons” | UB- Achmad Basuki, Agung Setia Budi | This research aims to propose a low-cost, efficient, and innovative IoT solution to track the location of buses without requiring the deployment of a GPS device in Malang, Indonesia. |
| 3a- Implementation and experiment of the research platform in Aceh Province, Indonesia for “An IoT-based transport data collection and analytics framework using Bluetooth proximity beacons” | USK- Rahmad Dawood, Masduki Khamdan | This research aims to propose a low-cost, efficient, and innovative IoT solution to track the location of buses without requiring the deployment of a GPS device in Aceh, Indonesia. |
| Code- Title | Lead | Description |
| 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” | 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. |
| 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” | 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. |
| 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” | 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. |
| 3b- 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” | USM- Yung-Wey Chong, Mohd Nadhir Bin Ab Wahab, Lim Gin Keat | 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. |