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A Service-Based Architecture for Enabling UAV Enhanced Network Services

January 7, 2023/in Publikasi /by Informatika

A Service-Based Architecture for Enabling UAV Enhanced Network Services

by Oussama Bekkouche, Konstantinos Samdanis, Miloud Bagaa, Tarik Taleb

Institute of Electrical and Electronics Engineers (IEEE)

This article provides an overview of enhanced network services, while emphasizing the role of UAVs as core network equipment with radio and backhaul capabilities. Initially, we elaborate the various deployment options, focusing on UAVs as airborne radio, backhaul and core network equipment, pointing out the benefits and limitations. We then analyze the required enhancements in the SBA to support UAV services including UAV navigation and air traffic management, weather forecasting and UAV connectivity management. The use of airborne UAVs network services is assessed via qualitative means, considering the impact on vehicular applications. Finally, an evaluation has been conducted via a testbed implementation, to explore the performance of UAVs as edge cloud nodes, hosting an ACS function responsible for the control and orchestration of a UAV fleet.

Privacy in Neural Network Learning: Threats and Countermeasures

January 7, 2023/in Publikasi /by Informatika

Privacy in Neural Network Learning: Threats and Countermeasures

by Shan Chang, Chao Li

Institute of Electrical and Electronics Engineers (IEEE)

Algorithmic breakthroughs, the feasibility of collecting huge amount of data, and increasing computational power, contribute to the remarkable achievements of NNs. In particular, since Deep Neural Network (DNN) learning presents astonishing results in speech and image recognition, the amount of sophisticated applications based on it has exploded. However, the increasing number of instances of privacy leakage has been reported, and the corresponding severe consequences have caused great worry in this area. In this article, we focus on privacy issues in NN learning. First, we identify the privacy threats during NN training, and present privacy-preserving training schemes in terms of using centralized and distributed approaches. Second, we consider the privacy of prediction requests, and discuss the privacy-preserving protocols for NN prediction. We also analyze the privacy vulnerabilities of trained models. Three types of attacks on private information embedded in trained NN models are discussed, and a differential privacy-based solution is introduced.

Psychological resilience correlates with EEG source-space brain network flexibility

January 7, 2023/in Publikasi /by Informatika

Psychological resilience correlates with EEG source-space brain network flexibility

by Véronique Paban, Julien Modolo, Ahmad Mheich, Mahmoud Hassan

MIT Press – Journals

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach

January 7, 2023/in Publikasi /by Informatika

AI-Empowered Maritime Internet of Things: A Parallel-Network-Driven Approach

by Tingting Yang, Jiacheng Chen, Ning Zhang

Institute of Electrical and Electronics Engineers (IEEE)

As one of the key technologies for realizing a fully digitalized world, the Internet of Things (IoT) requires ubiquitous connections across both land and sea. However, due to lack of infrastructure such as optical fibers and base stations, maritime communications inevitably face a highly complex and heterogeneous environment, which greatly challenges the connection reliability and traffic steering efficiency for future service-oriented maritime IoT. With the recent burgeoning application of artificial intelligence (AI) in many fields, an AI-empowered autonomous network for maritime IoT is envisioned as a promising solution. However, AI typically involves training/learning processes, which require realistic data/environment in order to obtain valuable outcomes. To this end, this article proposes the parallel network, which can be regarded as the “digital twin” of the real network and is responsible for realizing four key functionalities: self-learning and optimizing, state inference and network cognition, event prediction and anomaly detection, and knowledge database and snapshots. We then explain how various AI methods can facilitate the operation of the parallel- network-driven maritime network. A case study is provided to demonstrate the idea. Research directions are also outlined for further studies.

Brain network similarity: methods and applications

January 7, 2023/in Publikasi /by Informatika

Brain network similarity: methods and applications

by Ahmad Mheich, Fabrice Wendling, Mahmoud Hassan

MIT Press – Journals

Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a “network of networks” that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.

Graph convolutional network for fMRI analysis based on connectivity neighborhood

January 7, 2023/in Publikasi /by Informatika

Graph convolutional network for fMRI analysis based on connectivity neighborhood

by Lebo Wang, Kaiming Li, Xiaoping P. Hu

MIT Press – Journals

There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.

Security Analysis of a Space-Based Wireless Network

January 7, 2023/in Publikasi /by Informatika

Security Analysis of a Space-Based Wireless Network

by Daojing He, Xuru Li, Sammy Chan, Jiahao Gao, Mohsen Guizani

Institute of Electrical and Electronics Engineers (IEEE)

With the gradual deployment of the spacebased wireless network, security risks in the data communication between satellites and even the internal structure of a satellite become extremely important. In this article, we use the satellite’s internal communications security as an example to illustrate the space network security threats. We first provide a summary of the security requirements of space-based wireless networks. Then three typical attack approaches for satellite platforms based on MIL-STD-1553B bus are described. Subsequently, we present some attack simulation results and suggest some protective mechanisms.

Data-Driven Network Intelligence for Anomaly Detection

January 7, 2023/in Publikasi /by Informatika

Data-Driven Network Intelligence for Anomaly Detection

by Shengjie Xu, Yi Qian, Rose Qingyang Hu

Institute of Electrical and Electronics Engineers (IEEE)

Data-driven network intelligence will offer a robust, efficient, and effective computing system for anomaly detection in cyber security applications. In this article, we first summarize the current development and challenges of network intelligence for anomaly detection. Based on the current development, we propose a data-driven intelligence system for network anomaly detection. With the support of extended computing, storage, and other resources to the network edge, fog computing is incorporated into the design of the system. The proposed system consists of three major components: fog enabled infrastructure, fog enabled artificial intelligence (AI) engine, and threat intelligence. Fog enabled infrastructure provides efficient and effective computing resources for parallel computing and data storage. The fog enabled AI engine produces optimal learning models for threat detection, and enables efficient model update both locally and globally. Threat intelligence offers real-time network monitoring and cyber threat detection. We demonstrate that the proposed data-driven network intelligence system achieves high detection accuracy and provides efficient computational performance.

An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic

January 7, 2023/in Publikasi /by Informatika

An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic

by Xiaoxuan Zhang, Jing Ran, Jize Mi

Institute of Electrical and Electronics Engineers (IEEE)

As the Internet integrates with social life closely, various cyber threats pose a huge challenge to Intrusion Detection Systems (IDS). The performance of IDS based on traditional machine learning did not meet our expectations. In this paper, we propose an intrusion detection model based on Convolutional Neural Network (CNN). Before CNN training, Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) algorithm is applied to balance the network traffic. We use NSL-KDD dataset to evaluate the model. The proposed SMOTE-ENN-based CNN IDS model achieves an accuracy of 83.31%. Furthermore, the detection rates of User to Root (U2R) and Remote to Local (R2L) attacks are significantly improved. Results show that SMOTE-ENN-based CNN IDS outperforms the previous IDS model.

5G and Beyond: Smart Devices as Part of the Network Fabric

January 7, 2023/in Publikasi /by Informatika

5G and Beyond: Smart Devices as Part of the Network Fabric

by B.aldomero Coll-Perales, Javier Gozalvez, Juan Luis Maestre

Institute of Electrical and Electronics Engineers (IEEE)

5G networks mainly rely on infrastructure-centric cellular solutions to address data traffic and service demands. Continuously scaling infrastructure- centric cellular networks is not exempt from challenges, and beyond 5G networks should consider the active coexistence and coordination of infrastructure-centric and device-centric wireless networks. Device-centric wireless networks will build from D2D and MCNs. Device-centric wireless networks can push the limits of edge computing and networking to smart devices (including smartphones, vehicles, machines and robots), and exploit their mobile computing, storage and connectivity capabilities. These capabilities can be more efficiently utilized using demand-driven opportunistic networking that establishes the connections between devices and nodes not just based on their presence, but also on their capacity to support the requested demand and services. This article presents results from experimental field tests that demonstrate the cellular spectral efficiency gains that can be achieved from the combined use of device-centric wireless communications and demand-driven opportunistic networking. The field trials demonstrate that these technologies can improve the cellular spectral efficiency of conventional cellular communications by up to a factor of 4.7 and 12 in outdoor pedestrian and vehicular scenarios, respectively, under the evaluated scenarios and conditions. These gains have been obtained using 4G and IEEE 802.11 technologies. However, the potential of device-centric wireless networks is not constrained to any particular radio interface. The results presented in this article provide empirical evidences that further motivate progressing toward a new paradigm where edge networking capabilities are moved to smart mobile devices that become part of the network fabric, and can opportunistically and locally integrate network management functions to ensure that sufficient resources are placed where the demand arises.

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