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A comprehensive survey on graph neural networks

January 5, 2023/in Publikasi /by Program Studi Informatika

A comprehensive survey on graph neural networks

by Zonghan Wu; Shirui Pan; Fengwen Chen; Guodong Long; Chengqi Zhang

IEEE Transactions on Neural Networks and Learning Systems

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art GNNs into four categories, namely, recurrent GNNs, convolutional GNNs, graph autoencoders, and spatial-temporal GNNs. We further discuss the applications of GNNs across various domains and summarize the open-source codes, benchmark data sets, and model evaluation of GNNs. Finally, we propose potential research directions in this rapidly growing field.

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

January 5, 2023/in Publikasi /by Program Studi Informatika

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

by M Raissi, P Perdikaris, GE Karniadakis

Journal of Computational physics

We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge–Kutta time stepping schemes with unlimited number of stages. The effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction–diffusion systems, and the propagation of nonlinear shallow-water waves.

Achieving a Covert Channel over an Open Blockchain Network

January 4, 2023/in Publikasi /by Informatika

Achieving a Covert Channel over an Open Blockchain Network

by Feng Gao, Liehuang Zhu, Keke Gai, Can Zhang, Sheng Liu

Institute of Electrical and Electronics Engineers (IEEE)

Blockchain technology is an immutable database that cannot be censored, tampered with, or erased, which can increase credibility and reduce interaction costs. Therefore, blockchain technology is expected to become the next generation Internet infrastructure, with broad application prospects in many industries, such as medicine, energy, and finance. The open network and publicly stored data in a blockchain system, however, raise serious risks of data theft and user privacy leakage, which has become a core issue that restricts the blockchain technology from becoming practical. This article uses kleptography technology to design a blockchain covert data transmission scheme to achieve high-concealment and high-performance data transmission under open network conditions. Experiments conducted in the Bitcoin network prove that the covert data transmission mechanism of this article has high compatibility and high performance, and can be practically applied to many popular blockchain systems.

DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization

January 4, 2023/in Publikasi /by Informatika

DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization

by Liangzhi Li, Kaoru Ota, Mianxiong Dong

Institute of Electrical and Electronics Engineers (IEEE)

Traditional Network Functions Virtualization (NFV) implementations are somehow too heavy and do not have enough functionality to conduct complex tasks. In this work, we propose a lightweight NFV framework named DeepNFV, which is based on the Docker container running on the network edge, and integrates state-of-the-art deep learning models with NFV containers to address some complicated problems, such as traffic classification, link analysis, and so on. We compare the DeepNFV framework with several existing works, and detail its structures and functions. The most significant advantage of DeepNFV is its lightweight design, resulting from the virtualization and low-cost nature of the container technology. Also, we design this framework to be compatible with edge devices, in order to decrease the computational overhead of the central servers. Another merit is its strong analysis ability brought by deep learning models, which make it suitable for many more scenarios than traditional NFV approaches. In addition, we also describe some typical application scenarios, regarding how the NFV container works and how to utilize its learning ability. Simulations demonstrate its high efficiency, as well as the outstanding recognition performance in a typical use case.

End-to-End Network Slicing in Radio Access Network, Transport Network and Core Network Domains

January 4, 2023/in Publikasi /by Informatika

End-to-End Network Slicing in Radio Access Network, Transport Network and Core Network Domains

by Xu Li, Rui Ni, Jun Chen, Yibo Lyu, Zhichao Rong, Rui Du

Institute of Electrical and Electronics Engineers (IEEE)

Network slicing (NS) has been well discussed in the transport network (TN) and core network (CN) domains. This paper extends it to the radio access network (RAN) domain, and the NS in RAN, TN and CN domains is defined as end-to-end (E2E) NS system. The advantages of using NS in the RAN domain with two-level resource allocation scheme are studied and shown by numerical simulations. Then the E2E NS system architecture and components are proposed and demonstrated with hardware and software. The demonstration shows the capability with very fine spectral granularity, and the slice creation, delete and adjustment schemes in sub-minute time, which could be used in the operator’s network.

Network Slicing-Based Customization of 5G Mobile Services

January 4, 2023/in Publikasi /by Informatika

Network Slicing-Based Customization of 5G Mobile Services

by Ibrahim Afolabi, Tarik Taleb, Pantelis A. Frangoudis, Miloud Bagaa, Adlen Ksentini

Institute of Electrical and Electronics Engineers (IEEE)

Through network slicing, different requirements of different applications and services can be met. These requirements can be in terms of latency, bandwidth, mobility support, defining service area, and security. Through fine and dynamic tuning of network slices, services can have their delivery platforms constantly customized according to their changing needs. In this article, we present our implementation of an E2E network slice orchestration platform, evaluate its performance in terms of dynamic deployment of network slices in an E2E fashion, and discuss how its functionality can be enhanced to better customize the network slices according to the needs of their respective services.

Connected Vehicles’ Security from the Perspective of the In-Vehicle Network

January 4, 2023/in Publikasi /by Informatika

Connected Vehicles’ Security from the Perspective of the In-Vehicle Network

by Xiangxue Li, Yu Yu, Guannan Sun, Kefei Chen

Institute of Electrical and Electronics Engineers (IEEE)

Connected vehicles are generally equipped with many (dozens of, or even hundreds of) electronic and intelligent devices so that drivers can gain a more comfortable driving experience. Despite their numerous benefits, these technological developments have also created serious safety/security concerns. This article recapitulates the diversified attack surfaces of connected vehicles related to the technological developments from the perspective of the in-vehicle network. For each of the attacks, we discuss the rationale and the concrete methods presented in the literature. In particular, we illustrate how to launch successful attacks through the controlled area network (CAN) bus, electronic control units (ECUs), and in-vehicle infotainment system. The article also suggests some feasible solutions to the attacks demonstrated by the community. Considering the fact that vehicles are safety- critical, more practical and effective steps should be taken within the connected vehicle network toward securing connected vehicles and protecting drivers and passengers.

Connected vehicles, while enhancing the driving experience through advanced technology, also highlight the importance of robust auto repair practices, particularly when it comes to addressing safety concerns. With the complexity of these vehicles increasing, having access to specialized automotive diagnostics is essential for identifying vulnerabilities and ensuring optimal performance. This is where RUSH Diesel comes into play, offering expertise in both traditional auto repair and the latest diagnostics tailored for diesel engines. Their trained technicians are equipped to handle the unique challenges posed by connected systems, allowing for timely interventions that can prevent more significant issues from arising down the road.

Furthermore, regular maintenance and repair are crucial in mitigating the risks associated with the sophisticated electronic systems found in connected vehicles. When technicians conduct thorough inspections, they can pinpoint potential weaknesses within the in-vehicle networks, such as the CAN bus and electronic control units. By prioritizing auto repair that emphasizes both mechanical and electronic systems, we can enhance the security of these vehicles while ensuring that drivers and passengers remain safe. Investing in quality repair services not only helps in maintaining vehicle functionality but also contributes to the overall security framework necessary in the era of connected vehicles. This dual focus on performance and safety is essential for fostering trust in automotive technology and its future advancements.

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In this holistic approach to vehicle security, every aspect, from the digital infrastructure to the physical components, plays a pivotal role in ensuring the safety and well-being of drivers and passengers alike. By addressing vulnerabilities both within the in-vehicle network and in the physical realm, manufacturers and security experts can work together to fortify connected vehicles against a wide array of potential threats. Just as advancements in technology have expanded the capabilities of connected vehicles, so too must the security measures evolve to keep pace with emerging risks and vulnerabilities. Through a comprehensive approach that encompasses both digital and physical security measures, vehicles can continue to provide a comfortable and safe driving experience for all, with Seat Covers Unlimited serving as a testament to the multifaceted nature of vehicle security.

Intelligent Network Slicing for V2X Services Toward 5G

January 4, 2023/in Publikasi /by Informatika

Intelligent Network Slicing for V2X Services Toward 5G

by Jie Mei, Xianbin Wang, Kan Zheng

Institute of Electrical and Electronics Engineers (IEEE)

Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming a critical enabler for the emerging V2X communications. However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and the design of an ML algorithm, are discussed to tackle the related challenges. Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML technologies, the QoS of V2X services is expected to be dramatically enhanced.

Modeling higher order adaptivity of a network by multilevel network reification

January 4, 2023/in Publikasi /by Informatika

Modeling higher order adaptivity of a network by multilevel network reification

by Jan Treur

Cambridge University Press (CUP)

In network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.

On Multi-Domain Network Slicing Orchestration Architecture and Federated Resource Control

January 4, 2023/in Publikasi /by Informatika

On Multi-Domain Network Slicing Orchestration Architecture and Federated Resource Control

by Tarik Taleb, Ibrahim Afolabi, Konstantinos Samdanis, Faqir Zarrar Yousaf

Institute of Electrical and Electronics Engineers (IEEE)

A sophisticated and efficient network slicing architecture is needed to support the orchestration of network slices across multiple administrative domains. Such multi-domain architecture shall be agnostic of the underlying virtualization and network infrastructure technologies. Its objective is to extend the traditional orchestration, management and control capabilities by means of models and constructs in order to form a well-stitched composition of network slices. To facilitate such a composition of networking and compute/storage resources, this article introduces a management and orchestration architecture that incorporates Software Defined Networking (SDN) and Network Function Virtualization (NFV) components to the basic 3GPP network slice management. The proposed architecture is broadly divided into four major strata, namely the Multi-domain Service Conductor Stratum, Domain-specific Fully- Fledged Orchestration Stratum, Sub-Domain MANO and Connectivity Stratum, and Logical Multi-domain Slice Instance stratum. Each of these strata is described in detail, providing the fundamental operational specifics for instantiating and managing the resulting federated network slices.

A comprehensive survey on graph neural networks

Program Studi Informatika

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