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Detecting Phishing Scams on Ethereum Using Graph Convolutional Networks with Conditional Random Field 24

Detecting Phishing Scams on Ethereum Using Graph Convolution...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Hou, Wenhan Cui, Bo Li, Ru College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Hohhot China

ISBN: (纸本)9798350319934

As an innovative technology of great significance, blockchain has been widely used in various walks of life. Meanwhile, scams have experienced rapid growth on the blockchain, the phishing scam is a classic fraud among them. Ethereum is the largest blockchain platform that supports smart contracts, and its ecosystem has been under serious threat due to phishing scams. Consequently, phishing scam detection is particularly critical for Ethereum to avoid economic loss. However, accounts on Ethereum are represented as strings without features, which brings great difficulties for detection. To combat this challenge, we propose an effective method based on Graph Convolutional Network (GCN) with Conditional Random Field (CRF) to detect phishing scams. Firstly, we process accounts and their neighbors with transaction records to build transaction graphs. Due to lack of portrait information, we adopt DeepWalk to provide initial features for each node. then graph representations are learned through GCN with CRF. the extensive experiments show that the proposed model performs better than comparison methods on both precision and recall, which indicates that our method can effectively identify phishing scams on Ethereum. © 2022 IEEE.

关键词： Blockchain

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Artificial Intelligence based Facial Emotion Recognition with Deep Neural GAN Augmentation 8

Artificial Intelligence based Facial Emotion Recognition wit...

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8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Menaka, D. Priya, D. Karthika Lenus, C. Reeda Ashwini, A. Shermila, P. Josephin Prathaban, Banu Priya Noorul Islam Centre for Higher Education Department of Electronics and Instrumentation Engineering Kumaracoil India Easwari Engineering College Department of Artificial Intelligence and Data Science Ramapuram Chennai India S.A.Engineering College Department of Physics Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India R.M.K.College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Networking and Communication Chennai India

ISBN: (纸本)9798350367904

Facial recognition system in computer vision is widely used in human-computer interface and clinical diagnostics. Nonetheless, achieving high accuracy in this task still poses a challenge due to issues of inadequate amount and imbalance of data to train the models as well as issues of generalization and robustness. the findings of this research propose the use of Generative Adversarial Network (GAN) data augmentation for face emotion classification specifically for this approach. the proposed strategy is based on the generation of artificial 'emotional' images on the face using Deep Neural Self Attention GANs (DNSAGANs). this way, to capture all the variations of facial expressions the GAN takes a large number of images and thus makes the augmented data richer and more realistic. this larger data set is then used to train a deep learning architecture that performs the face emotion classification thereby enabling the network to learn enhanced discriminative features. To collect face photos the FER2013 dataset is used. the training process, entrained the generator and discriminator to minimize the mean squared error while the discriminator was simultaneously trained to robustly distinguish generated images from real ones. Altogether, the proposed Adam optimizer was more accurate than Adagrad and Adadelta for all epochs. the percentage of accuracy is about 88.64 % for 100 epochs using the proposed optimizer techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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Joint Task Scheduling and Load Balancing in computing Power Network-enabled Edge computing Systems 8

Joint Task Scheduling and Load Balancing in Computing Power ...

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8th IEEE international conference on Computer and Communications, ICCC 2022

作者： Xie, Renchao Zhu, Han Tang, Qinqin Chen, Qingxia Qiao, Shi Huang, Tao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Purple Mountain Laboratories Nanjing China Qian Xuesen Laboratory of Space Technology Beijing China

ISBN: (纸本)9781665450515

Edge computing is a promising computing paradigm to enable next-generation delay-sensitive services which are not conceivable in traditional cloud-based architectures. Due to the challenges of unbalanced workloads and limited single-point resources in edge computing systems, the collaboration among different edge nodes is an important issue to realize balanced and on-demand computing capabilities of edge systems. With the continuous extension of the network, large-scale and complex networking brings new challenges to edge collaboration. Recently, computing Power Network (CPN) has been proposed as a possible next-generation network construction to realize optimized resource allocation among multiple heterogeneous edge nodes. In this paper, a practically deployable CPN -enabled edge computing framework is proposed. We focus on realizing computing power sharing and load balancing among heterogeneous computing nodes through flexible task scheduling, which is an attempt to achieve the interworking of computing and network capabilities. Considering the dynamic and continuous changes of the system, a learning-based algorithm is proposed to give optimal scheduling decisions. Finally, the simulation results show the effectiveness and superiority of the proposed task scheduling scheme. © 2022 IEEE.

关键词： Edge computing

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th international conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. this paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. the experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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Real time vehicle dynamic path reconstruction algorithm based on AVI checkpoint data 8

Real time vehicle dynamic path reconstruction algorithm base...

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8th international conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Huang, Lichen Wu, Ruoqian Zhang, Ji Xu, Yanqing Zhou, Yong Chen, Zhenwu Shenzhen Urban Transport Planning Center CO. LTD Shenzhen518021 China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

this paper proposes a real-time vehicle dynamic path reconstruction algorithm based on AVI checkpoint data. Firstly, the algorithm follows an optimized KSP algorithm for constructing optional pathsets and a spatiotemporal multi-constraint path selection algorithm during the real-time vehicle driving process to reconstruct the path and status of the vehicle at each intersection. Secondly, a trip chain separation method based on the K-means clustering algorithm is used to complete the separation of vehicle paths. Finally, in order to improve the real-time performance of path reconstruction, an architecture based on a distributed system is adopted. the algorithm is tested with actual data, and the results indicated commendable performance in both accuracy and real-time responsiveness. © 2024 SPIE.

关键词： Roads Reconstruction algorithms Data processing distributed computing Mathematical optimization Data analysis Data acquisition Sampling rates Sensors Transportation

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FTL-EDGE: A Feature Federation Transfer Learning Algorithm for Single Microgrid Edge computing 8

FTL-EDGE: A Feature Federation Transfer Learning Algorithm f...

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8th international conference on Power and Renewable Energy, ICPRE 2023

作者： Xu, Qi Shi, Xiaohong Shanghai Maritime University College of Information Engineering Shanghai China

ISBN: (纸本)9798350328813

With the continuous development of power system and the advancement of intelligence, Microgrid Cluster (MGC) has attracted wide attention as an emerging energy supply and management model. the problem of cooperative and optimal operation of microgrid clusters under privacy protection arises for microgrids belonging to different interest entities. the optimal integration of resources within a single microgrid (MG) is also of positive significance for the overall microgrid cluster. Edge computing, as a distributed computing model, provides an effective data processing and decision support capability for MG. However, the current MG "end-edge-cloud"architecture ignores the privacy of information transfer and communication efficiency between different devices. therefore, this paper proposes a feature federation transfer learning algorithm FTL-EDGE for single microgrid edge computing, where each device in the microgrid requests the central server to download the basic model in the edge gateway, and the new device model can be converted into a target domain model with limited device-specific data using transfer learning techniques. Different devices in the microgrid train their own target domain models locally and upload features to the cloud. the cloud aggregates the domain model features of each device, performs scenario derivation and global policy search, and sends the policies to each device. the experimental results show that this method achieves resource cost optimization under a single microgrid and improves the overall economic efficiency of the microgrid and reduces the communication overhead compared with the methods of independent operation and deep reinforcement learning. © 2023 IEEE.

关键词： Edge computing (EC) Federated Learning (FL) Microgrid (MG) Microgrid Cluster (MGC) Transfer Learning (TL)

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Detect and Alleviate DDoS Attacks in Cloud Environment 1

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8th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Kulwanth, Bandi Srinivasarengan, V. Anish, Peddinte Abirami, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789819976331

ISBN: (纸本)9789819976324

the Internet is now a crucial aspect of our lives, and distributed computing has grown significantly in size, capability, and complexity, becoming an essential element of our daily lives. there are numerous vulnerabilities in distributed network computing, including viruses, worms, distributed denial of service (DDoS), DoS, and more. One of the more newsworthy attacks recently has been distributed denial-of-service attacks. Attacks that cause denials of service (DoS) have developed and had an influence on the Internet infrastructure. A DDoS attack aims to prevent the targeted users from accessing a computer resource. In this paper, we perform a comprehensive survey of detecting DDoS packets via couple of machine learning algorithms such as Random Forest, Gradient Descent, decision tree, Multi-layer Perceptron, and radial basis function using the CIC-DDoS2019 dataset. We also combine all our machine learning models into one hybrid model via an ensemble classifier. After that, we experiment with a DDoS attack on a local server via Ufonet and capture the data packets during the attack using the Wireshark tool. the captured data will be converted to a CSV file and can be given as input to machine learning models. the results show that each machine learning algorithm is predicting DDoS packets incoming into the server. the experimental results show that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack. Finally, directions for future research work have been pointed out. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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distributed Position Estimation of a Mobile Object Under a WSN Using a Robust UFIR Filter with Consensus on Estimates 8th

Distributed Position Estimation of a Mobile Object Under a W...

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8th international conference on computing, Control and Industrial Engineering, CCIE 2024

作者： Vazquez-Olguin, Miguel Ibarra-Manzano, Oscar Pale-Ramon, Eli G. Ortega-Contreras, Jorge A. Shmaliy, Yuriy S. Department of Electronics Engineering Universidad de Guanajuato Salamanca Mexico

ISBN: (纸本)9789819769339

Position estimation of a moving object benefits from using wireless sensor networks (WSN) technology, as each node can provide redundant and independent information about the state dynamics of the object. On the other hand, low cost and limited energy sensors are affected by external factors such as electromagnetic interference, weather, damaged sensors, and landscape itself, which man cause unstable data transmission resulting in missing and corrupted data. To overcome this drawback, robust estimators are required. In this paper, we develop a robust distributed unbiased finite impulse response filter (dUFIR) to solve the position estimation problem with consensus on estimates and under unstable data. It is shown that the robust dUFIR filter is suitable for position estimation of a mobile object, offering robust estimates under missing and corrupted data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Impulse response

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Dynamic Clustering-based Sharding in distributed Deduplication Systems 8

Dynamic Clustering-based Sharding in Distributed Deduplicati...

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8th IEEE/ACM international Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-8 2022

作者： Zhou, Peng Zou, Xiangyu Xia, Wen Harbin Institute of Technology ShenZhen China

ISBN: (纸本)9781665463379

Witha massive upsurge in data, combining dedu-plication with distributed storage continuously suffer from a low deduplication ratio when providing the corresponding through-put. It is because distributed storage requires sharding data on different nodes, while global deduplication needs eliminating re-dundancies in a unified view. In this paper, we present clustering-based sharding method, D-Shard, in distributed deduplication storage systems that leads to a comparable deduplication effi-ciency on a single system while supporting a high throughput. To achieve that, we try to make that duplicates are located at the same node as much as possible with two key techniques. First, using Dynamic K-Means approach to cluster super-blocks, then extracting every cluster center feature as the anchor point for sharding;Second, Construct a secondary deduplication index based on the Compact Hamming Index. If a super-block mapping to the middle of two anchor points across nodes, then super-blocks adjacent to the feature are re-deduplicated in the cluster offline. Currently, preliminary results show that super-block clustering is convergent, and routing strategy based on anchor points can achieve a higher deduplication ratio compared to the state-of-the-art approach and the throughput of system has been greatly improved. © 2022 IEEE.

关键词： Cloud storage

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A Federated Learning Approach for distributed Human Activity Recognition 8

A Federated Learning Approach for Distributed Human Activity...

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Concone, Federico Ferdico, Cedric Lo Re, Giuseppe Morana, Marco Univ Palermo Dipartimento Ingn Viale SciEd 6 I-90128 Palermo Italy

ISBN: (纸本)9781665481526

In recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' personal devices are employed as the basis of the sensing infrastructure. Experimental analysis was performed to evaluate the effectiveness of the architecture as compared with a centralized approach, under different settings. Results demonstrate the versatility and functionality of the proposed solution.

关键词： Federated Learning distributed computing Machine Learning Human Activity Recognition

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