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Privacy, security and Policies: A Review of Problems and Solutions with Blockchain-Based Internet of Things Applications in Manufacturing Industry 18

Privacy, Security and Policies: A Review of Problems and Sol...

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18th international conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th international conference on Future networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information Technology, SEIT 2021

作者： Pal, Kamalendu City University of London Northampton Square LondonEC1V 0HB United Kingdom

Internet of Things (IoT) aims to simplify the collection of the distributed data in a global manufacturing business, sharing and processing of information and knowledge across many collaborating partners using appropriate information system architecture. As with the IoT, convergence with blockchain technology that processes data, privacy and security-related issues, and data policies (e.g., regulatory compliance) may apply to data and software artefacts. Besides, blockchain technology could contribute to the more intelligent and flexible handling of transactional data through appropriate convergence with IoT technology in supporting data integration and processing. This paper examines this hybrid architecture's privacy, security, and policy-related issues to appreciate the convergence and understand the integration of IoT and blockchain technology. This paper primarily identifies common trends focusing on relevant topics in blockchain-based IoT technology research and by highlighting the need to explore security issues further - for example, data privacy challenges in the manufacturing industry. © 2021 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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A Novel Delegated Proof of Work Consensus Protocol

A Novel Delegated Proof of Work Consensus Protocol

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2021 international conference on Artificial Intelligence for Cyber security systems and Privacy, AI-CSP 2021

作者： Kara, Mostefa Laouid, Abdelkader Bounceur, Ahcene Lalem, Farid Alshaikh, Muath Kebache, Romaissa Sayah, Zaoui El Oued University LIAP Laboratory El Oued39000 Algeria University of Western Brittany UBO Lab-STICC UMR CNRS Brest6285 France Saudi Electronic University College of Computing and Informatics Computer Science Department Riyadh11673 Saudi Arabia

ISBN: (纸本)9781665467148

Blockchain is a new technology for storing and transmitting information evolving without centralized control, it uses peer-to-peer networks and allows the constitution of replicated and distributed registers, these registers are secured thanks to cryptography by the linking of blocks to each other. As part of solving the challenges that exist to develop this technology, we present this article introducing significant improvements to this efficient mechanism. These improvements consist of proposing a consensus algorithm called Delegated Proof of Work (DPoW). Thanks to this algorithm, we offer a significant reduction in the very large amount of energy currently consumed in the mining process, we reduce the domination of an element with great computing power, we reinforce security against the 51 % attack, reducing its percentage to 67 %, and we also reduce the influence of false identities created within the network (Sybil Attack). On the other hand, we propose a consensus model for permissioned blockchain, this model is based on the encryption of random values and offers total security to reach a consensus. © 2021 IEEE.

关键词： Blockchain

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Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation

Knowledge-aware Multi-view Cross Learning for Edge-based Col...

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international conference on Parallel and distributed systems (ICPADS)

作者： Yang Dai Shunmei Meng Huanhuan Gu Nan Liu Longchuan Tu School of Cyberspace Security NanJing University of Science and Technology Nanjing China State Key Lab. for Novel Software Technology NanJing University Nanjing China Department of Computer Science and Engineering NanJing University of Science and Technology Nanjing China Nanjing Sinovatio Technology Co. Ltd. Nanjing China

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users’ and items’ embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention Network assigning various weights to different neighbors. Secondly, Graph Neural Network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios.

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Sequential Heuristic Model for DDOS Attack Detection in Software-Defined Network

Sequential Heuristic Model for DDOS Attack Detection in Soft...

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international Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Tooba Shaikh Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Software Defined Networking (SDN) is a new networking paradigm that uses a central controller to streamline network administration while increasing flexibility and evolution. Because of its centralized management and programmable nature, SDN is susceptible to distributed denial of service (DDoS) attacks which can exploit security weaknesses. These attacks can overload the switch and controller's memory, drain server resources and network bandwidth, disrupting normal user access. This research proposes an anomaly detection technique employing higher-order statistics, such as skewness and kurtosis, to detect DOS and DDoS attacks, specifically focusing on TCP Flood and UDP Flood in an SDN environment. This algorithm efficiently recognizes when an attack occurred and determines the exact moment it occurred. We examined the performance of our algorithm using a dataset transmitted between hosts in an SDN environment, testing it with various window sizes. Through a comparative analysis, we demonstrated the superior capability of our proposed methodology in detecting malicious flows within SDN-based networks. To assess performance, we employed detection rate and FPR (False Positive Rate) as evaluation parameters and validated the effectiveness of our approach utilizing a configuration employed within the Mininet network emulator.

关键词： Switches Traffic control Denial-of-service attack Kurtosis security Floods Software defined networking computer crime Higher order statistics Testing

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent systems (ICTIS), international conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling Data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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Effective DDoS Detection through Innovative Algorithmic Approaches in Machine Learning

Effective DDoS Detection through Innovative Algorithmic Appr...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Sutrisno Untung Rahardja Sutarto Wijono Teguh Wahyono Irwan Sembiring Indrastanti R Widiasari Department of Information Technology Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

A distributed Denial of Service (DDoS) attack disrupts online services by overwhelming them with unauthorized traffic, posing significant network security risks. Traditional detection techniques struggle with the evolving nature of these attacks. This study proposes an innovative machine learning approach to accurately identify and prevent DDoS attacks, enhancing detection speed while minimizing false alarms. The optimized model involves network traffic preprocessing, training on large datasets, and performance verification under various attack scenarios. The research reveals the proposed algorithm detects DDoS attacks with 98% accuracy and an average response time of 2 seconds, reducing false positives by 40% compared to conventional systems. Additionally, the algorithm is scalable, accommodating new attack forms and diverse networks. Implementing these models improves understanding of attack patterns, leading to better coping mechanisms over time. The developed machine learning algorithm provides an effective, adaptive solution for DDoS detection.

关键词： Training Machine learning algorithms Machine learning Organizations Telecommunication traffic Denial-of-service attack Real-time systems Time factors computer crime Resilience

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Blockchain-based Resource Registration in Constrained IoT Environments

Blockchain-based Resource Registration in Constrained IoT En...

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Advances in Electronics, Control and Communication systems (ICAECCS), international conference on

作者： Kheireddine Zaghouani Badis Djamaa Ali Yachir Artificial Intelligence and Virtual Reality Laboratory ´ Ecole Militaire Polytechnique (EMP) PO BOX 17 Bordj-El-Bahri Algiers Algeria

The interconnection of devices in Internet of Things (IoT) networks results in a ubiquitous exchange of resources between clients and servers. This interaction involving multicast discovery requests in classical Service oriented Architectures is hardly applicable for IoT nodes that go through sleep phases to conserve energy. CoRE Resource Directory (RD) provides a CoAP-based solution, including a central node where the server registers its resources, and the client requests them. Nevertheless, the centralization of operations makes this node a point of failure vulnerable to DDoS attacks and causes a mistrusted authentication between RD and endpoint devices. The Blockchain (BC) is a valuable solution for building decentralized, trustless peer-to-peer networks that enable reliable and non-repudiable data exchange. However, its application in IoT environments is a real challenge due to the computing and storage constraints of the devices. In this paper, we propose a novel architecture based on a distributed network of RDs acting as validators in a permissioned blockchain. The aim is to safeguard the resource registration phase and define the type of data stored in the BC ledger. Three approaches are proposed, implemented, and evaluated by exploiting Hyperledger Iroha Blockchain and a CoAP library called CoAPthon. The differences between approaches regarding storage size, usability, and security are highlighted. The assessment results show the impact of each approach and discuss the possibility of a combined solution concluding with their applicability in the proposed architecture to further secure the CoAP resource registration.

关键词： computer architecture Trustless services Peer-to-peer computing Blockchains Service-oriented architecture Internet of Things Servers

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20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

20th International Conference on Data Science, ICDATA 2024, ...

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20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031858550

The proceedings contain 37 papers. The special focus in this conference is on Data Science. The topics include: Trust as a Classification Tool: Analyzing Collaboration in Senate Floor Speeches on Gun Legislation Post-Uvalde and Sandy Hook;an Application of Convolutional Neural networks to Chaotic systems;artificial Intelligence Data Reduction Algorithm for Streaming Readout in High Energy Physics Experiment;user Behavior Based Implicit Personality Detection in Recommendation systems;proactive Management of Delays in the French Railway Network: A Seasonal Machine Learning Based Approach;CNN to BiLSTM: Enhancing Setswana Named Entity Recognition;Assessing the Impact of Homelessness on COVID-19 Hospitalization Rates in Patients with Underlying Medical Conditions Through Explainable AI;leveraging Ensemble Learning Paradigms for B2B E-Commerce Fraud Detection;exploring Equity: Integrating Knowledge Graphs in Fairness Testing Methodologies;explainable Machine Learning Approach for Intelligent Edits of Medicaid Home Healthcare Services Claims;authAttLyzer-V2: Unveiling Code Authorship Attribution Using Enhanced Ensemble Learning Models and Generating Benchmark Dataset;Real-Time AI Voice Clone Detection: A Deep Learning Approach to Safeguard Authenticity;Enhancing security with Automated Boom Gate Access Through License Plate Recognition Utilising YOLOv8 Model;generating Culturally Appropriate Avatars;NLP-Guided Synthesis: Transitioning from Sequential Programs to distributed Programs;Generative AI Techniques for the Simulation of Groundwater Well Data at Hanford Site;Enhancing Financial Analysis with Generative AI: Utilizing Large Language Models for Efficient Data Extraction;Leveraging NLP and Large Language Models for Clinical Documentation Improvement: A Medical AI Chatbot Approach;Harnessing LLMs to Build an Autonomous Marketing Agent;Generative AI Use in One Health, One System as Healthcare is Global;dataDock: An Open Source Data Hub for Research;big Data Techniques i

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Trace Anomaly Detection for Microservice systems via Graph-based Semi-supervised Learning

Trace Anomaly Detection for Microservice Systems via Graph-b...

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international conference on computer Supported Cooperative Work in Design

作者： Shuai Ding Yuepeng E Jiye Zhang Liangxiong Li Lei Zhang Jingguo Ge Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Beijing Institute of Computer Technology and Applications Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Microservice architectures have become mainstream for cloud-native applications, and distributed tracing is widely used to ensure system observability. Trace is essentially an aggregated data with multimodal attributes (e.g., performance metric, invocation log, and topology relationships). Existing methods typically do not adequately consider the complex correlations and interactions between the different modalities of trace, whereas fusing multimodal data into a unified analysis provide the possibility to improve performance. On the other hand, most of the existing methods are trained in an unsupervised manner and cannot utilize historical data. Therefore, this article proposes a trace anomaly detection method using graph-based semi-supervised learning, TraceGSAD. It extracts features of trace from multiple modalities and train an improved message-passing neural network to fuse features for unified modeling and generate a graph-level representation. The end-to-end deep anomaly detection model trained in an semi-supervised manner, using only a small amount of labeled data to achieve superior performance. The evaluation on the microservice benchmarks show that TraceGSAD achieves a high precision, outperforming state-of-the-art trace anomaly detection approaches. It also demonstrates the effectiveness of multimodal fusion analysis as well as the use of empirical data in semi-supervised learning that can significantly improve anomaly detection performance.

关键词： Measurement Correlation Fuses Computational modeling Neural networks Microservice architectures Semisupervised learning

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A Secure Authenticated Message Transfer in Vehicle to Grid networks

A Secure Authenticated Message Transfer in Vehicle to Grid N...

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Electronics and Sustainable Communication systems (ICESC), 2020 international conference on

作者： L. Sowmiya Rajasekaran Arun Sekar Gnanasaravanan Subramaniam A. Purushothaman Mohit Tiwari Tripti Tiwari M E VLSI Design KPR Institute of Engineering and Technology Coimbatore India Department of ECE KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of technology and sciences Coimbatore India Department of ECE Hindusthan Institute of Technology Coimbatore Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi Department of Management Studies Bharati Vidyapeeth (Deemed to be University) Institute of Management and Research Delhi India

Due to the recent emerging trends in technology, Vehicle to Grid (V2G) networks plays a major role in smart grid system. Now a days using of electric vehicles has increased than the usage of fuel-based automobiles under transportation. Electric vehicles mainly use electricity instead of fuels that is distributed by the charging terminal. Generally, the charging terminal will be situated in gasoline stations or on road sides and electric vehicles get charged at the stations. In V2G communication networks, the communication takes place between the charging terminal and the electric vehicle will be affected by several cyber-attacks. In addition to that, the electric vehicle owner’s personal information should be kept secure. In this proposed system, charging terminal and electric vehicles initially register their identities at the control authority. Using of Anonymous mutual authentication scheme, the charging terminal and the electric vehicle authenticates with each other and establish protected communication between them. During Session Key Exchange protocol, the information passed from the charging terminal will be transferred to the electric vehicle with the help of session keys. Duplicate identity of the charging terminal and electric vehicle is created in order to protect the original identity of the users who are present in V2G networks. Proposed scheme gives strength against various security attacks and cost of the system is also reduced. The main aim of this proposed system is to maintain confidentiality and integrity among the V2G communication networks.

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