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Eclipse Attack Defense Method Based on distributed Storage and Reference Value System 23

Eclipse Attack Defense Method Based on Distributed Storage a...

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23rd IEEE international conference on Communication Technology, ICCT 2023

作者： Li, Lin Fu, Yiming Wang, Tao Zhao, Haili Mi, Rongxin Liu, Zhicheng Chen, Zixuan Feng, Hao Xiao, Yvchi National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China Changan Communication Technology Co. Ltd Beijing China

ISBN: (纸本)9798350325959

This paper proposes a blockchain protection method based on distributed storage and reference value systems to prevent solar eclipse attacks in blockchain networks. Despite the inherent reliability and security provided by the decentralization of blockchain to a certain extent, potential network attacks that pose significant threats still exist. In response to these representative network attacks, particularly eclipse attacks, traditional solutions have primarily focused on controlling access nodes. While ensuring security, these solutions hinder the joining of new nodes and subsequent expansion of network. Therefore, this paper introduces an extensible method based on a reference value system and distributed storage. The method transfers the risk of nodes in the network falling victim to eclipse attacks through the reference value system and significantly reduces this risk through distributed storage. Finally, we analyze the proposed solution by creating a simulated network environment. The analysis results demonstrate that the proposed solution effectively mitigates the impact of eclipse attacks. © 2023 IEEE.

关键词： blockchain distributed storage eclipse attack reference value system

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Influence of Access Points’ Height and High Signal Relation in WLAN Fingerprinting-Based Indoor Positioning systems’ Accuracy 1

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26th international conference on distributed computer and Communication networks: Control, Computation, Communications, DCCN 2023

作者： Nicholaus, Mrindoko R. Ruambo, Francis A. Masanga, Elijah E. Muthanna, Mohammed Saleh Ali Lashchev, Andrei Computer Science and Engineering Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of Information Systems and Technology Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Research and Development Department Aifrruis Laboratories Mbeya Tanzania United Republic of Institute of Computer Technologies and Information Security Southern Federal University Taganrog347922 Russia

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

Wireless Local Area Network (WLAN) Fingerprinting-based Indoor Positioning systems (IPS) offer several key advantages over other indoor positioning technologies, including cost-effectiveness with high accuracy and passive tracking of entities’ location without Global Positioning System (GPS) dependence. However, achieving a satisfactory accuracy performance of the WLAN fingerprinting-based IPS is still challenging, as it is affected by several factors, such as obstacles (e.g., walls and furniture) that can block or weaken wireless signals. Additionally, the IPS’s accuracy can be affected by environmental changes, such as the height, addition, or removal of a WLAN Access Point (AP) or changes in radio frequency interference. This paper presents a novel algorithm incorporating a probabilistic analytical model employing only high signal relations that mitigate the effect of low signal relation on the WLAN fingerprinting-based IPS, thus improving the accuracy performance. Furthermore, it analyses the impact of AP heights on the accuracy performance of the WLAN IPS. Upon extensive experiments, the proposed algorithm improves the accuracy of the IPS by an average of 32.74% in terms of Root Mean Square Error (RMSE). These results imply that incorporating APs’ height and high signal relations can significantly improve the accuracy performance of the WLAN fingerprinting-based IPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE international conference on Parallel and distributed systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong 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 Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE international conference on Parallel and distributed systems, ICPADS 2023

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

ISBN: (纸本)9798350330717

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. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Machine Learning Approach for Mitigating security Threats in IoT Environment 5

Machine Learning Approach for Mitigating Security Threats in...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Saini, Harnit Bala, Saroj Ida, S.Jhansi Kumar, K. Suresh Swetha, Shankerpally Divyabarathi, P. Ajay Kumar Garg Engineering College Department of Cse Ghaziabad India Ajay Kumar Garg Engineering College Department of Mca Ghaziabad India R.M.K Engineering College Department of Computer Science and Business Systems Kavaraipettai India Kongu Engineering College Department of Cse Erode India Cvr College of Engineering Department of Information Technology Telangana India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India

ISBN: (纸本)9798350321425

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection System (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated. © 2023 IEEE.

关键词： Denial-of-service attack

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Long-Distance Pipeline Intrusion Warning Based on Environment Embedding from distributed Optical Fiber Sensing 32nd

Long-Distance Pipeline Intrusion Warning Based on Environmen...

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32nd international conference on Artificial Neural networks (ICANN)

作者： Zhu, Chengyuan Pu, Yanyun Lyu, Zhuoling Qian, Jiren Yang, Kaixiang Zhejiang Univ Hangzhou 310007 Zhejiang Peoples R China Zhejiang Univ Huzhou Inst Huzhou 313000 Zhejiang Peoples R China Zhejiang Zheneng Nat Gas Operat Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9783031442032;9783031442049

Pipeline is one of the most important transportation modes of oil and gas. However, it is different from the single-scene environments in fields such as security and high-speed railway, as the environment along the length of long-distance pipelines is complex and variable, including plains, mountains, and lakes. Different geological conditions have different effects on the transmission of vibrations, which poses great challenges to the external perception of distributed optic fiber sensing (DOFS) systems. At the same time, the similarity of different external activity signal characteristics also greatly affects the recognition performance of DOFS systems. Mechanical construction activities along the pipeline have posed a serious threat to the safety of pipelines. In this paper, an intrusion warning ensemble model based on environmental embedding is developed on the latest novel distributed optic fiber hardware system (f-OTDR), named Surroundings-Embedding Ensemble Learning (SEEL). Environmental embedding technology captures the fine-grained differences in spatial environment between different defense areas. At the same time, ensemble learning technology reduces the negative impact of random noise and environmental interference. A large amount of data collected in the actual environment is used for comparison experiments. The results show that this model can achieve more accurate intrusion detection accuracy and has a wider practical application range by effectively fusing environmental information. In addition, the effectiveness of the new component designed is verified through ablation experiments.

关键词： Oil and gas pipeline f-OTDR Environment embedding Ensemble learning

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Multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning 24th

Multiple-Round Aggregation of Abstract Semantics for Secur...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Jiao Li, Xiong Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615445

Federated Learning (FL) provides a valuable framework that allows for the collaborative training of models across distributed networks while maintaining the privacy of the data involved. The concept of secure aggregation is crucial in preserving both the privacy and integrity of data within FL systems. However, the deployment of secure aggregation encounters significant barriers. Conventional secure aggregation methods are primarily designed for single-round operations, which can become prohibitively expensive in scenarios that require multiple rounds. Additionally, these methods often struggle in large-scale environments characterized by heterogeneous data and models. This paper introduces the Multiple-Round Aggregation of Abstract Semantics (MRAAS), a novel scheme designed to address the aforementioned limitations in secure aggregation within heterogeneous FL environments. The MRAAS scheme enhances security and reduces the need for extensive data exchanges by aggregating abstracted model semantics instead of raw model gradients, facilitating secure, multiple rounds of aggregation. Key innovations of MRAAS include an advanced abstraction technique, robustness against participant drop-out, and the use of recyclable secrets. These innovations collectively enhance system resilience and reduce the necessity for frequent cryptographic renewals. Our theoretical evaluations focus on the security features of MRAAS. Empirical validations conducted through comparative analyses utilizing two public datasets confirm the superiority of MRAAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Network and System security - 17th international conference, NSS 2023, Proceedings

Network and System Security - 17th International Conference,...

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Network and System security - 17th international conference, NSS 2023, Proceedings

ISBN: (纸本)9783031398278

The proceedings contain 21 papers. The special focus in this conference is on Network and System security. The topics include: Resilience of Blockchain Overlay networks;t3E: A Practical Solution to Trusted Time in Secure Enclaves;security Model for Privacy-Preserving Blockchain-Based Cryptocurrency systems;on the Design of a Misinformation Widget (MsW) Against Cloaked Science;security Analysis of Mobile Point-of-Sale Terminals;agnostic Label-Only Membership Inference Attack;SPoiL: Sybil-Based Untargeted Data Poisoning Attacks in Federated Learning;Privacy-Preserving Federated Learning with Hierarchical Clustering to Improve Training on Non-IID Data;Evaluating Rule-Based Global XAI Malware Detection Methods;The Effectiveness of Transformer-Based Models for BEC Attack Detection;New LDP Approach Using VAE;ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves;Decentralized SGX-Based Cloud Key Management;preface;RRML: Privacy Preserving Machine Learning Based on Random Response Technology;Modular Polynomial Multiplication Using RSA/ECC Coprocessor;KDRM: Kernel Data Relocation Mechanism to Mitigate Privilege Escalation Attack;provably Secure Blockchain Protocols from distributed Proof-of-Deep-Learning;spying on the Spy: security Analysis of Hidden Cameras.

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Towards a Resilient Multi-Agent Controller: Securing and Mitigating Overhead in Tactical SDN 11

Towards a Resilient Multi-Agent Controller: Securing and Mit...

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11th IEEE international conference on Software Defined systems, SDS 2024

作者： Rettore, Paulo H. L. Zisner, Philipp Lawisch, Marcio Kloth, Sean De Freitas, Edison Pignaton Sevenich, Peter Federal University of Rio Grande Do Aul Porto Alegre Brazil Bonn Germany University of Bonn Institute of Computer Science Bonn Germany

ISBN: (纸本)9798331518325

This study proposes a resilient multi-agent controller for Software-defined Tactical networks (SDTNs) to ensure both secure and available control plane communication. The methodology consists of a Cyber Defense Agent (CDA) with two security layers, where the first layer monitors the network and establishes a secure channel through blockchain, and the second layer monitors the network, extracts network features, detects and responds to distributed Denial-of-Service (DDOS) attack. Cooperating with the CDA, the Load Control Agent (LCA) is designed to reduce the overhead introduced by the OpenFlow and the blockchain on the control plane through a clustering approach. The control overhead per individual controller could be reduced when clustering is applied. The proof of concept was made by evaluating the network under additional control overhead, introduced through the CDA, which shows that the LCA can cooperate with the CDA and cluster the network to ensure a reliable control plane. © 2024 IEEE.

关键词： Denial-of-service attack

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25th international Symposium on Stabilization, Safety, and security of distributed systems, SSS 2023

25th International Symposium on Stabilization, Safety, and S...

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25th international Symposium on Stabilization, Safety, and security of distributed systems, SSS 2023

ISBN: (纸本)9783031442735

The proceedings contain 44 papers. The special focus in this conference is on international Symposium on Stabilization,Safety, and security of distributed systems. The topics include: Improved Paths to Stability for the Stable Marriage Problem;lattice Linearity of Multiplication and Modulo;the Fagnano Triangle Patrolling Problem (Extended Abstract);invited Paper: Monotonicity and Opportunistically-Batched Actions in Derecho;robust Overlays Meet Blockchains: On Handling High Churn and Catastrophic Failures;disconnected Agreement in networks Prone to Link Failures;where Are the Constants? New Insights on the Role of Round Constant Addition in the SymSum Distinguisher;invited Paper: Detection of False Data Injection Attacks in Power systems Using a Secured-Sensors and Graph-Based Method;KerberSSIze Us: Providing Sovereignty to the People;model Checking of distributed Algorithms Using Synchronous Programs;hierarchical Identity-Based Inner Product Functional Encryption for Unbounded Hierarchical Depth;brief Announcement: Efficient Probabilistic Approximations for Sign and Compare;meeting Times of Non-atomic Random Walks;minimum Algorithm Sizes for Self-stabilizing Gathering and Related Problems of Autonomous Mobile Robots (Extended Abstract);separation of Unconscious Colored Robots;forbidden Patterns in Temporal Graphs Resulting from Encounters in a Corridor;uniform k-Circle Formation by Fat Robots;brief Announcement: Rendezvous on a Known Dynamic Point in a Finite Unoriented Grid;brief Announcement: Crash-Tolerant Exploration by Energy Sharing Mobile Agents;time-Optimal Geodesic Mutual Visibility of Robots on Grids Within Minimum Area;the Fence Complexity of Persistent Sets;privacy in Population Protocols with Probabilistic Scheduling;dispersion of Mobile Robots in Spite of Faults;brief Announcement: Asynchronous Gathering of Finite Memory Robots on a Circle Under Limited Visibility;wait-Free Updates and Range Search Using Uruv;stand-Up Indulgent Gathering on Lines;offl

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