Development

Designing and Implementing Next-Generation Smart Meters

  1. Propose a new three-layer (Smart Meter-Concentrator-Meter Data Management System) smart meter security firmware update architecture and process. Use the encryption chip with IEC 62056-5-3:2017 Security Suite 1 to speed up the decryption time and reduce the computational burden of the electricity meter. It can do multi-party authentication, remote update and other work.
  2. Develop a high-efficiency detection system for analyzing the resistance to side-channel attacks of IoT devices, implementing countermeasures such as hiding and masking against side-channel attacks, and measure the resistance of these implementations to the latest deep learning attack methods.

Information security detection and protection mechanism of renewable energy equipment and systems

  1. Aiming at the potential weaknesses and loopholes of renewable energy networking equipment and grid-connected systems, establish a set of renewable energy system information security testing procedures, and develop inverters and data collectors that comply with California Rule 21 information security standards.
  2. Implement the cryptographic protocols (TLS, AES, ECDHE, ECDSA, etc.) in IEEE 2030.5 on various platforms of the smart inverter to ensure the connection and data security of the renewable energy network when it communicates with the server.

Critical Infrastructure Honeynets and Intrusion Detection Systems

  1. In cooperation with the National Information Communication Security Report Technology Service Center (NCCST) of the Executive Yuan, for the large amount of network traffic captured by NCCST, we can identify new types of attacks and predict attack types, thereby reducing the time cost of personnel detection and analysis, and quickly understand new attack methods and trends.
  2. Using industrial control equipment sensing data, combined with artificial intelligence and deep learning methods, develop an industrial control equipment data analysis system to predict the remaining useful lifetime of the equipment, abnormal detection and classification, as the basis for predictive maintenance scheduling.