Prof. Ir. Endra Joelianto, Ph.D.
Institut Teknologi Bandung (ITB), Bandung, Indonesia
Endra Joelianto is a Professor of Instrumentation and Control Research Group, Faculty of Industrial Technology, Bandung Institute of Technology (ITB), Indonesia. He is the Head of Artificial Intelligence, Control and Automation Laboratory, Engineering Physics and Instrumentation & Control Program, Faculty of Industrial Technology, Bandung Institute of Technology. He is also a Research Professor at the National Center for Sustainable Transportation Technology (NCSTT) and University Center of Excellence Artificial Intelligence on Vision, NLP & Big Data Analytics, and University Center of Excellence Defense and Security Technology at ITB. He is the author of more than 155 refereed publications and 1 book, the editor of 2 monographs. The main research results are Intelligence Transportation Systems (MIRA-SEED Fund, World Class Professor Program, Minister of Education, Culture, Research and Technology, ITB Research Program), Internet of Things for Dengue Fever Monitoring, Analysis and Warning Systems (Innovative Productive Research Funding, LPDP), Electric Vehicle Motor Control (Ministry of Education, Research and Technology, partially funded by USAID SHERA), Artificial Intelligence in Precision Farming (Indonesian Collaborative Research Program, World Class University), Gantry Crane Control Systems for Port Automation (International Collaborative Research-ITB, Minister of Education, Culture, Research and Technology). His research areas include control and hybrid systems, robust control, intelligent systems, automation systems, modeling and simulation, industrial IoT, swarm intelligence, computational intelligence, artificial intelligence, intelligent transportation systems and cyber security. He has been an invited speaker at international conferences and chaired several international conferences. He is a senior member of the IEEE.
Intelligent Transportation Systems with Learning Control
The increasing population growth in the world makes the current transportation system very important. Many people want the existing transportation system to run well with good service because the need to travel is an integral part of everyday life even in smart cities. Population growth and the ever-increasing number of vehicles have made congestion in urban areas and toll road networks, especially in big cities, inevitable. Factors that can cause congestion include an imbalance between vehicle volume and road capacity, infrastructure damage, errors in determining the allocation and range of green lights at each traffic light, and driver behavior in driving their vehicles. Recently, new technologies in transportation are rapidly developing, such as connected and automated vehicles, and shared mobility services. The rapid evolution of engineering brings great opportunities and challenges to improve existing and future sustainable urban transport systems. The successful implementation of intelligent transportation systems depends on many factors, including technical, operational and political aspects. Designing, testing and implementing effective intelligent transport applications requires new and multidisciplinary techniques.
Even so, congestion is still a very serious main problem in the urban transportation system because it has an impact on the performance and service of a transportation system in major cities in the world. To overcome congestion that occurs, an effective dynamic traffic management (DTM) system is also needed to reduce the level of congestion that affects mobility management by presenting solutions to reduce congestion and improve overall network performance. DTM allocates infrastructure and vehicle fleet usage temporally and spatially through dynamic signals. One strategy in DTM is to use traffic signal control by using a control system to regulate vehicles through traffic lights to produce traffic conditions with maximum output, unaccumulated queue length, and shorter travel time. Meanwhile, new sensing and data sources bring opportunities for data-driven applications to better reflect the features and dynamics of urban transport systems. The increasingly available data and the complexity of mathematical models also pose challenges for large-scale computations. Therefore, new control algorithms with data-driven and learning methods are becoming a significant new area of research.
Prof. Dr. Igor V. Kotenko
St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS)
Igor V. Kotenko is a Chief Scientist and Head of Research Laboratory of Computer Security Problems of the St. Petersburg Federal Research Center of the Russian Academy of Sciences. He is also Professor of ITMO University, St. Petersburg, Russia, and Bonch-Bruevich Saint-Petersburg State University of Telecommunications. He is the author of more than 800 refereed publications, including 25 books and monographs. Main research results are in artificial intelligence, telecommunication, cyber security, including network intrusion detection, modeling and simulation of network attacks, vulnerability assessment, security information and event management, verification and validation of security policy. Igor V. Kotenko was a project leader in the research projects from the European Office of Aerospace Research and Development, EU FP7 and FP6 Projects, HP, Intel, F-Secure, Huawei, etc. The research results of Igor Kotenko were tested and implemented in multitude of Russian research and development projects, including grants of Russian Science Foundation, Russian Foundation of Basic Research and multitude of State contracts. He has been an invited speaker on multitude of international conferences and workshops. He has chaired several international conferences.
Visual analytics for cyber security: State-of-the-Art, Challenges and Applications
At present, the use of visual analytics methods has become almost the gold standard in tasks related to data exploration in different application areas. The presence of visual analytics components in business intelligence systems largely determines the competitive advantage and demand for the product on the market. This is primarily due to the fact that visual analytics combines the advantages of interactive data visualization and intelligent data analysis methods. The talk considers visual analytics research and development in the cyber security domain. Main challenges and future trends in visual analytics are analyzed. The existing models and methods of visual data analysis for solving cyber security problems are discussed. The effectiveness of visual analysis is demonstrated by the examples of the developed visual analytics tools in domains of traffic analysis, attack modeling, security assessment, detection of financial violations. This research is being supported by the grant of RSF #21-71-20078 in SPC RAS.