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Enhancing Small Object Detection in Aerial Imagery Based on Strong Feature Extraction and Batch Dimension

Enhancing Small Object Detection in Aerial Imagery Based on ...

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international Joint conference on Neural networks (IJCNN)

作者： Bingnan Cao Di Wang Ying Guo Hu Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The Institute of Data Science City University of Macau Macau China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection is a fundamental component of computer vision, playing a pivotal role in various applications. However, the accurate detection of small and densely distributed objects remains a challenging problem in this field. This challenge is particularly exacerbated in the context of aerial imagery, characterized by its distinctive bird’s-eye view, intricate backgrounds, and the variability in object appearances. This paper addresses these persistent challenges in object detection, with a focus on the specific difficulties posed by aerial images. We propose a deformable end-to-end object detection with transformers (DETR)-based framework to enhance small object detection accuracy, ultimately contributing to improved computer vision capabilities in domains like remote sensing, surveillance, and autonomous aerial systems. Firstly, in order to aggregate the entire input sequence information in the backbone network and improve the detection accuracy of small objects, we propose DMCA based on deformable features and attention mechanisms. Secondly, in order to capture and model the relationships between samples for dense pixel-level representations in small objects and improve the detection accuracy of small objects, we try to introduce BatchEncoder by implementing an encoder in the batch dimension. Experimental results show that, compared to the baseline, our method significantly improves the accuracy of small object detection in aerial images. The processing and analysis of a large amount of remote sensing data require powerful computing power. The computing power Internet can integrate the scattered computing resources to provide flexible and scalable computing power to meet the computing needs of different sizes and types.

关键词： computer vision Accuracy Surveillance Object detection Detectors Transformers Feature extraction

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Design and Implementation of a distributed Firewall Management System for Improved security

Design and Implementation of a Distributed Firewall Manageme...

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Roedunet international conference (RoEduNet)

作者： Andrei-Daniel Tudosi Adrian Graur Doru Gabriel Balan Alin Dan Potorac Department of Computers Electronics and Automation Stefan cel Mare University of Suceava Suceava Romania

Network security is the practice of protecting computer networks and devices from unauthorized access, attacks, and threats. These threats can come from a variety of sources, including hackers, malware, and external attacks, and they can compromise the confidentiality, integrity, and availability of sensitive data and systems. A network security management system is a system that is designed to help protect and secure a network from these threats. In this context, this article presents an approach to create a management system for a distributed firewall, which provides multiple benefits that will be presented in the following sections. The script obtains the CPU utilization and network bandwidth data of the WAN interface for each virtual machine and optimization algorithm, and then outputs the retrieved information. The application programming interface (APD is used to access the data from the virtualization platform and to modify the optimization process on the firewall. In this way, the solution is capable to monitor the entire network and make decisions based on parameters to ensure a high-performance security. The proposed script manages virtual machines inside the virtualization platform using an API to retrieve data and change the optimization algorithm of the firewall.

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Hashgraph-Based Model Parameter Management for Reliable and Secure Deep Learning

Hashgraph-Based Model Parameter Management for Reliable and ...

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2025 IEEE international conference on Consumer Electronics, ICCE 2025

作者： Roh, Eunsung Choi, Jinheock Park, Younghoon Seo, Seung-Woo Big Data Research Center for New Value Creation Sookmyung Women's University Seoul Korea Republic of Department of Electric Engineering Seoul National University Seoul Korea Republic of Division of Computer Science Sookmyung Women's University Seoul Korea Republic of

ISBN: (纸本)9798331521165

Deep learning networks have rapidly developed across various fields, with several high-performing models already being applied in real-world applications. However, even with identical network architectures, variations in parameter values can lead to significantly different outputs, making the security of these parameters crucial for maintaining network performance. Despite their importance, limited research has focused on securing deep learning network parameters. In recent years, distributed ledger technologies, such as blockchain and hashgraph, have been extensively studied. Their applications extend beyond cryptocurrencies to include public services, voting, supply chains, and insurance. This paper proposes a novel approach to enhancing the security of deep learning network parameters by leveraging Hedera Hashgraph, providing a more secure foundation for their deployment and commercialization. With the security offered by hashgraph, model parameters are safely stored, ensuring the reliability and trustworthiness of deep learning systems. Additionally, when these stored parameters are used for further training, such as in transfer learning, the results are expected to be reliable. We demonstrate that our proposed system secures the parameters effectively, guaranteeing both security and reliability in deep learning. © 2025 IEEE.

关键词： Deep learning

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Online Scheduling Unbiased distributed Learning over Wireless Edge networks 27

Online Scheduling Unbiased Distributed Learning over Wireles...

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27th IEEE international conference on Parallel and distributed systems, ICPADS 2021

作者： Han, Ziyi Zhou, Ruiting Pang, Jinlong Cao, Yue Tan, Haisheng School of Cyber Science and Engineering Wuhan University China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781665408783

To realize high quality smart IoT services, such as intelligent video surveillance in Auto Driving and Smart City, tremendous amount of distributed machine learning jobs train unbiased models in wireless edge networks, adopting the parameter server (PS) architecture. Due to the large datasets collected geo-distributedly, the training of unbiased distributed learning (UDL) brings high response latency and bandwidth consumption. In this paper, we propose an online scheduling algorithm, Okita, to minimize both the latency cost and bandwidth cost in UDL. Okita schedules UDL jobs at each time slot to jointly decide the execution time window, the amount of training data, the number and the location of concurrent workers and PSs in each site. To evaluate the practical performance of Okita, we implement a testbed based on Kubernetes. Extensive experiments and simulations show that Okita can reduce up to 60% of total cost, compared with the state-of-the-art schedulers in cloud systems. © 2021 IEEE.

关键词： security systems

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Enhancing Network Resilience against DDoS Attacks: Critical Node Identification using Load based k-shell Analysis

Enhancing Network Resilience against DDoS Attacks: Critical ...

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Knowledge Engineering and Communication systems (ICKECS), international conference on

作者： L Manjula G T Raju Department of CSE Ramaiah Institute of Technology Visvesvaraya Technological University Belagavi India Department of CSE SJC Institute of technology Visvesvaraya Technological University Belagavi India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Although IoT technology streamlines services by linking various nodes via the Internet, this interconnect brings forth security hurdles. These challenges arise from the integration itself, rendering IoT infrastructure susceptible to cyber threats such as distributed Denial of Service (DDoS) attacks. DDoS attacks can disrupt IoT functionality, causing financial loss. Intruders mainly concentrate to overwhelm the network connecting nodes. So, the connecting nodes must be safeguarded. Investing mitigation policies on all nodes is not cost effective and hence critical nodes must be identified through ranking procedure. The prioritization of connecting nodes to enhance network resilience has been largely overlooked. This paper proposes a novel Load-based K shell algorithm for ranking critical nodes in heterogeneous networks by identification of critical nodes with the graph structure and node/edge load feature aggregation. The method is implemented and evaluated using Python module and NS3 simulation of an IoT network with various sensor nodes. The results depict the proposed approach can identify the most crucial nodes in the network, which can help to mitigate the impact of DDoS attacks and improve network resilience.

关键词： Prevention and mitigation Heuristic algorithms Traffic control Denial-of-service attack Rendering (computer graphics) Heterogeneous networks Topology

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CS-NIDS: Network Intrusion Detection Based on Cross-Silo Federated Learning

CS-NIDS: Network Intrusion Detection Based on Cross-Silo Fed...

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Electrical Engineering (ICEENG), international conference on

作者： Maged Abdelaty Informatics Research Institute (IRI) City of Scientific Research and Technological Applications (SRTA-City) Alexandria Egypt

ISBN: (数字)9798331519018

ISBN: (纸本)9798331519025

Network intrusions are malicious attacks that target the confidentiality, integrity, and availability of computer networks. Network Intrusion Detection System (NIDS) can identify these intrusions and provide timely alerts to protect networked systems. Traditional centralized NIDS face several limitations, including privacy concerns and limited scalability and availability. Federated Learning (FL), a distributed machine learning approach, offers a solution by enabling information sharing across multiple data sources without the need to centrally store or share that data. In this paper, we propose CS-NIDS, a network intrusion detection system based on cross-silo federated learning, designed to address the limitations of centralized NIDS. We compare several aggregation algorithms, namely FedAvg, FedProx, FedAvgM, and FedAdam, to assess their effectiveness in the context of CS-NIDS. The evaluation is conducted using the CIC-IDS 2017 dataset, a well-known benchmark for network intrusion detection tasks. Our results show that FedAdam outperforms other aggregation algorithms in terms of intrusion detection accuracy, making it the most suitable choice for CS-NIDS.

关键词： Training Privacy Accuracy Federated learning Scalability Soft sensors Network intrusion detection Information sharing Network security Optimization

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Strategic Renewable Integration for Resilient Power systems: Insights from Kefallonia Island

Strategic Renewable Integration for Resilient Power Systems:...

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international conference on High Voltage Engineering and Application, ICHVE

作者： E.G. Goulioti E.M. Voumvoulakis V.T. Kontargyri C.A. Christodoulou N.P. Papanikolaou School of Electrical and Computer Engineering High Voltage and Electrical Measurement Laboratory National Technical University of Athens 9 Iroon Polytechniou Str. Zografou Campus Athens Greece Hellenic Electricity Distribution Operator S.A. 20 Perraivou & 5 Kallirrois Str. Athens Greece Department of Electrical and Electronics Engineering Faculty of Engineering University of West Attica Athens Greece Department of Electrical and Computer Engineering Electrical Machines Laboratory Democritus University of Thrace Xanthi Greece

ISBN: (数字)9798350374988

ISBN: (纸本)9798350374995

The current evolution of power systems from a unidirectional to a multi-directional power flow system, in which distributed Energy Resources (DERs) are increasing, directly impacts the way they are being operated to ensure the security and quality of electrical supply and the resilient performance of the electrical grid as a whole. Additionally, the functional capability of the active distribution network is continually challenged by extreme weather conditions and unforeseen events. As such, the imperative for developing and implementing advanced resilience strategies within distribution networks has never been more critical, ensuring they are not only robust but also agile in the face of increasingly unpredictable challenges. In the wake of extreme events, fast recovery and system stability of the network are investigated. Optimal utilization of available resources is the key to system resilience. Studying the performance of the power network of Kefallonia island in two case studies (single-fault and multiple-fault), the results prove that among the available resources that are required for the resilience and the minimization of the catastrophic consequences due to extreme events, microgrids should be incorporated as a building block for the future grid.

关键词： Renewable energy sources Microgrids Power system stability Minimization Stability analysis Planning security

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference

Multilingual Knowledge Graph Completion via Multimodal Decou...

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international conference on Parallel and distributed systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance.

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A Graph Convolutional networks-Based DDoS Detection Model

A Graph Convolutional Networks-Based DDoS Detection Model

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Annual IEEE systems conference

作者： Braden J. Saunders Patrice Kisanga Glaucio H.S. Carvalho Isaac Woungang Department of Computer Science Brock University St. Catharines Canada Department of Computer Science Toronto Metropolitan University Toronto Canada Department of Computer Science and Engineering Brock University St. Catharines Canada

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

Network attacks have exponentially increased over the last years and, seriously, impacting fundamental aspects of our modern society at all levels, i.e., individual, critical infrastructure, and national security. To counterattack these cyber threats, several approaches for detecting or preventing them have been investigated. Ultimately, these approaches culminated in the design and development of Intrusion Detection systems (IDSs) and Intrusion Prevention systems (IPSs). From a detection standpoint, intelligent engines using Artificial Intelligence, Machine learning, and more recently deep learning have played a fundamental role in improving the detection capabilities of such systems. distributed Denial of Service (DDoS) is an attack that causes loss of availability by overwhelming the target system with malicious packets that preclude legitimate users from accessing the system resources. Despite the development of IDS and IPS, successful DDoS attacks have continued to rise. To address this growing and threatening concern, this paper proposes the design of a Graph Convolutional Network (GCN)- empowered DDoS detection system. The proposed GCN model consists of three hidden layers, each with 128 neurons, and its effectiveness is validated by experiments using the UNB CIC- IDS 2017 DDoS dataset, showing that it achieves an accuracy, precision, recall, and F1-score of 99.95%, 99.95%, 99.95%, and 99.95%, respectively, which are promising results.

关键词： Deep learning Convolutional codes Adaptation models Accuracy Intrusion detection Predictive models Denial-of-service attack

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Effective Intrusion Detection in Heterogeneous Internet-of-Things networks via Ensemble Knowledge Distillation-Based Federated Learning

Effective Intrusion Detection in Heterogeneous Internet-of-T...

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IEEE international conference on Communications (ICC)

作者： Jiyuan Shen Wenzhuo Yang Zhaowei Chu Jiani Fan Dusit Niyato Kwok-Yan Lam Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

With the rapid development of low-cost consumer electronics and cloud computing, Internet-of- Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT devices are often susceptible to cyber attacks due to their open deployment environment and limited computing capabilities for stringent security controls. Hence, Intrusion Detection systems (IDS) have emerged as one of the effective ways of securing IoT networks by monitoring and detecting abnormal activities. However, existing IDS approaches rely on centralized servers to generate behaviour profiles and detect anomalies, causing high response time and large operational costs due to communication overhead. Besides, sharing of behaviour data in an open and distributed IoT network environment may violate on-device privacy requirements. Additionally, various IoT devices tend to capture heterogeneous data, which complicates the training of behaviour models. In this paper, we introduce Federated Learning (FL) to collaboratively train a decentralized shared model of IDS, without exposing training data to others. Furthermore, we propose an effective method called Federated Learning Ensemble Knowledge Distillation (FLEKD) to mitigate the heterogeneity problems across various clients. FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results on the public dataset CICIDS2019 demonstrate that the proposed approach outperforms local training and traditional FL in terms of both speed and performance and significantly improves the system's ability to detect unknown attacks. Finally, we evaluate our proposed framework's performance in three potential real-world scenarios and show FLEKD has a clear advantage in experimental results.

关键词： Training Privacy Federated learning Smart cities Intrusion detection Training data Data models

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