C-ITS Environment Modeling and Attack Modeling

As technology advances, cities are evolving into smart cities, with the ability to process large amounts of data and the increasing complexity and diversification of various elements within urban areas. Among the core systems of a smart city is the Cooperative-Intelligent Transport Systems (C-ITS). C-ITS is a system where vehicles provide real-time information to drivers about surrounding traffic conditions, sudden stops, falling objects, and other accident risks through roadside base stations. It consists of road infrastructure, C-ITS centers, and vehicle terminals. However, as smart cities integrate many elements through networks and electronic control, they are susceptible to cybersecurity issues. In the case of cybersecurity problems in C-ITS, there is a significant risk of safety issues arising. This technical document aims to model the C-ITS environment and the services it provides, with the purpose of identifying the attack surface where security incidents could occur in a smart city environment. Subsequently, based on the identified attack surface, the document aims to construct attack scenarios and their respective stages. The document provides a description of the concept of C-ITS, followed by the description of the C-ITS environment model, service model, and attack scenario model defined by us.Comment: in Korean Language, 14 Figures, 15 Page

AI-based Attack Graph Generation

With the advancement of IoT technology, many electronic devices are interconnected through networks, communicating with each other and performing specific roles. However, as numerous devices join networks, the threat of cyberattacks also escalates. Preventing and detecting cyber threats are crucial, and one method of preventing such threats involves using attack graphs. Attack graphs are widely used to assess security threats within networks. However, a drawback emerges as the network scales, as generating attack graphs becomes time-consuming. To overcome this limitation, artificial intelligence models can be employed. By utilizing AI models, attack graphs can be created within a short period, approximating optimal outcomes. AI models designed for attack graph generation consist of encoders and decoders, trained using reinforcement learning algorithms. After training the AI models, we confirmed the model's learning effectiveness by observing changes in loss and reward values. Additionally, we compared attack graphs generated by the AI model with those created through conventional methods.Comment: in Korean Language, 8 Figures, 14 Page

The epitaxial growth of GaN and A1GaN/GaN Heterostructure Field Effect Transistors (HFET) on Lithium Gallate (LiGaO₂) substrates

Ph.D.April S Brow

Heterogeneous integration: From substrate technology to active packaging

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Human behavior analysis by means of multimodal context mining

There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels

Enhanced write performance of a 64-Mb phase-change random access memory

The write performance of the 1.8-V 64-Mb phase-change random access memory (PRAM) has been improved, which was developed based on 0.12-mu m CMOS technology. For the improvement of RESET and SET distributions, a cell current regulator scheme and multiple step-down pulse generator were employed, respectively. The read access time and SET write time are 68 ns and 180 ns, respectively

A 0.18-mu m 3.0-V 64-Mb nonvolatile phase-transition random access memory (PRAM)

A nonvolatile 64-Mb 1T1R phase-transition random access memory (PRAM) has been developed by fully integrating chalcogenied storage material (GST: Ge2Sb2Te5) into 0.18-mum CMOS technology. To optimize SET/RESET distribution, 512-kb sub-array core architecture was proposed, featuring meshed ground line and separated SET/RESET control schemes. Random read access time, random SET and RESET write access times were measured to be 60 ns, 120 ns, and 50 ns, respectively, at 3.0-V supply voltage with 30degreesC