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19th international Symposium on distributed Computing and Artificial Intelligence, DCAI 2022

ISBN: (纸本)9783031232091

The proceedings contain 22 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: computer Vision: A Review on 3D Object Recognition;An IoUT-Based Platform for Managing Underwater Cultural Heritage;overview: security in 5G Wireless systems;a Study on the Application of Protein Language Models in the Analysis of Membrane Proteins;visualization for Infection Analysis and Decision Support in Hospitals;An Intelligent and Green E-healthcare Model for an Early Diagnosis of Medical Images as an IoMT Application;towards Highly Performant Context Awareness in the Internet of Things;adaptive System to Manage User Comfort Preferences and Conflicts at Everyday Environments;ML-Based Automation of Constraint Satisfaction Model Transformation and Solver Configuration;race Condition Error Detection in a Program Executed on a Device with Limited Memory Resources;the Impact of Covid-19 on Student Mental Health and Online Learning Experience;Threat Detection in URLs by Applying Machine Learning Algorithms*;an Approach to Simulate Malware Propagation in the Internet of Drones;the Use of Corporate Architecture in Planning and Automation of Production Processes;Towards Ontology-Based End-to-End Domain-Oriented KBQA System;TFEEC: Turkish Financial Event Extraction Corpus;denial of Service Attack Detection Based on Feature Extraction and Supervised Techniques;automating the Implementation of Unsupervised Machine Learning Processes in Smart Cities Scenarios;Intelligent Model Hotel Energy Demand Forecasting by Means of LSTM and GRU Neural networks;explainable Artificial Intelligence on Smart Human Mobility: A Comparative Study Approach.

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A Batch-Service Queueing Assisted Blockchain System for Supply Chain Management

A Batch-Service Queueing Assisted Blockchain System for Supp...

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international conference on Data Science and Network Engineering, ICDSNE 2023

作者： Dash, Bibhuti Bhusan De, Utpal Chandra Pattnayak, Parthasarathi Satapathy, Rabinarayan Behera, Sibananda Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed to be University Bhubaneswar India Faculty of Emerging Technologies Sri Sri University Cuttack India Trident Academy of Creative Technology Bhubaneswar India

ISBN: (纸本)9789819967544

In recent years, blockchains have attracted a great deal of interest from researchers, engineers, and institutions. Additionally, the implementation of blockchains has begun to revitalisea large number of applications, such as e-finance, smart homes, smart health, social security, logistics, and so on. As supply chains become increasingly global, management and control become more complicated. Blockchain technology is establishing promise in tackling several supply chain management (SCM) concerns as a distributed digital ledger platform that ensures security, traceability, and transparency. Government, societal, and consumer pressures have encouraged us to consider how blockchain can help us meet our sustainability goals and improve supply chain sustainability. True blockchain-led supply chain and corporate transformation is still in its early phases. This chapter examines the possible application of blockchain technology (BT) and smart contracts to SCM. In order to study the operations of blockchain based supply chain management a queueing based model is developed. It assesses the measures of the model, such as the mean transactions per unit time, the mean transactions in the unconfirmed transaction pool, the mean time required for transaction confirmation, and the average © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Detection and Analysis of DDoS Attack Using a Collaborative Network Monitoring Stack 16

Detection and Analysis of DDoS Attack Using a Collaborative ...

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16th international conference on security of Information and networks, SIN 2023

作者： Moosa, Muhammad Aashiq Vangujar, Apurva K. Mahajan, Dnyanesh Pramod National College of Ireland Department of Computing Dublin Ireland School of Computer Science It University College Cork Cork Ireland

ISBN: (纸本)9798350343212

In recent years, distributed Denial of Service (DDoS) attacks have become common and have evolved into a standard method of targeting entities. These attacks can be used to make money or to damage the network and systems of an organisation. The majority of recent research in this field has focused on implementing various machine learning (ML) strategies to detect and thwart these attacks. Even though these techniques have a high rate of accuracy, they occasionally struggle to detect zero-day vulnerabilities or manage intense network traffic. This investigation shifts its focus from DDoS detection to the creation of a cost-effective network monitoring system. The intended solution will provide real-time visibility into a simulated network environment on Graphical Network Simulator-3 (GNS3) and incorporate a machine learning (ML) engine for enhanced DDoS detection via network monitoring tools such as Prometheus, Zabbix, and Grafana. This initiative seeks to provide organisations with a comprehensive, cost-effective solution that extends beyond DDoS mitigation by emphasising a more cost-effective and efficient monitoring system. During the implementation of the project, a few technical obstacles prevented the integration of certain components into the solution, as will be described in the paper. However, an integrated solution and collaborative design will increase the solution's ability to detect more sophisticated DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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Performance Evaluation of Consensus Algorithms for Permissioned Blockchain networks 14

Performance Evaluation of Consensus Algorithms for Permissio...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kalwad, Triveni Aramani, Vinuta Attar, Needa Fatima Raikar, Neha Narayan, D.G. Pawar, Manjula Shettar, Pooja Kle Technological University Department of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350335095

The ever-evolving technological landscape demands systems that can ensure trust, security, transparency, and traceability. To meet these requirements, blockchain technology has emerged that uses decentralized ledgers to keep transaction records. It utilizes cryptography to interconnect devices, and once recorded, data stored on a blockchain cannot be altered by any unauthorized parties, thereby enhancing its security features. Consensus in blockchain refers to the process of achieving agreement among a distributed network of nodes on the current state of the blockchain ledger. No centralized body is in charge of validating and approving transactions in a decentralized blockchain network. To guarantee that every node in the network has a consistent and accurate copy of the ledger, the consensus mechanism is used. By simulating the time to attain consensus with the NS-3 network simulator, we assess the performance of the consensus algorithms Raft and PBFT in this paper. This paper considers the effectiveness of the Raft and PBFT consensus algorithms by simulating the time taken to reach consensus using the NS-3 network simulator. Based on the evaluation results, it was found that the performance of PBFT is on average 30% better than RAFT with varying message sizes, transaction speeds, and the number of nodes. © 2023 IEEE.

关键词： Blockchain

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A Cutting-Edge Method on Big Data Analytics Recent Developments for Business Operations and security Management 6th

A Cutting-Edge Method on Big Data Analytics Recent Developme...

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6th international conference on Smart Computing and Informatics, SCI 2024

作者： Razzhivin, Oleg Anatolevich Ollanazarov, Bekmurod Davlatmuratovich Okagbue, Hilary Izuchukwu Andryushchenko, Iryna Evgenievna Department of Theory and Methods of Physical Culture and Life Safety of Elabuga Institute Kazan Federal University Kazan Russia Department of Tourism Urgench State University Urgench Uzbekistan Sydani Institute for Research and Innovation Sydani Group Abuja Nigeria Department of Mathematics Covenant University Ota Nigeria Khorezm University of Economics Urgench Uzbekistan Mamun University Khiva Uzbekistan Department of Finance Banking and Insurance National University "Zaporizhzhia Polytechnic" Zaporizhzhia Ukraine

ISBN: (纸本)9789819619801

The management of a corporation could be significantly enhanced by the use of big data. There has not been enough statistical investigation to determine the importance of large data in the malignancy of the highly functional and methodical investigations. Extremely massive data compilations featuring a more intricate and diversified structure are referred to as big data. These traits usually lead to more challenges when it comes to data extraction, analysis, storing, and further operations. "Big data analytics" is the process of examining enormous volumes of complicated information to uncover hidden patterns or connections. However, there is an obvious conflict between the expanding usage of big data as well as its safety and confidentiality. It addresses the most crucial elements of computer network management and organization, including privacy, in order to meet the demanding security standards of big data applications. Since more and more individuals share personal data and materials on social networks as well as public cloud services via their PCs and smartphones, it is crucial to find a solution to this problem. Consequently, creating a secure foundation for social networks has become a hot research subject. One of each of the case study sections in this chapter deals with this last subject. In addition, Big Data computing systems are not appropriate for the use of conventional security measures like firewalls and demilitarized areas. But if information is not sufficiently protected against threats like phishing and hacking. These difficulties include vulnerabilities in open databases;safeguards against hacking and data leakage;and so on. When managing large-scale, dispersed data sets, safeguarding and privacy requirements provide a serious challenge to monitoring and tracking data use and access in a dynamic, distributed context. This effort aims to investigate the issues that arise in attempting to maintain the security and privacy of large amounts of data, a

关键词： Operations research

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Addressing Data Poisoning and Model Manipulation Risks using LLM Models in Web security

Addressing Data Poisoning and Model Manipulation Risks using...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： Shraddha Pradipbhai Shah Aditya Vilas Deshpande School of Computer and Information Sciences University of the Cumberlands Kentucky USA

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Data poisoning and model manipulation represent significant threats to cybersecurity, where adversaries intentionally inject malicious data into training sets, leading to compromised machine learning models. This study explores the efficacy of using Large Language Models (LLMs) to detect and mitigate such risks in cybersecurity. We propose a novel LLM-based framework that analyzes incoming data streams, identifies anomalies indicative of poisoning, and performs real-time model correction to maintain the integrity of security systems. Leveraging the generative and analytical capabilities of LLMs, our approach dynamically adapts to evolving attack vectors by detecting subtle changes in data distributions. We evaluated the framework on a cybersecurity dataset with known data poisoning incidents, achieving an accuracy of 97.5% in identifying poisoned data and reducing model manipulation attempts by 85% compared to conventional machine learning models. Additionally, our method exhibited a 30% improvement in response time to attacks, ensuring faster detection and mitigation. The study demonstrates that integrating LLMs into cybersecurity systems can significantly enhance resilience against sophisticated adversarial attacks.

关键词： Adaptation models Accuracy Large language models Prevention and mitigation Machine learning Data models Real-time systems Time factors computer security Resilience

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Application of Deep Learning for Intrusion Detection and Cyber-Attack Detection Using Internet of Things networks

Application of Deep Learning for Intrusion Detection and Cyb...

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2023 international conference on Data Science and Network security, ICDSNS 2023

作者： Gautam, Anjali Rathare, Saloni Bhatnagar, Gunjan Ikhar, Sharyu Dari, Sukhvinder Singh Gupta, Varun Kumar School of Engineering and Technology K. R. Mangalam University Department of Computer Science and Engineering Haryana Gurugram India Invertis University Department of Law Uttar Pradesh Bareilly India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Viit Pune Department of Aids India Pune India School of Computer Science and Applications Iimt University Meerut India

ISBN: (纸本)9798350301595

The ability to deploy such systems throughout a region, or perhaps a nation, will increase as IoT infrastructure develops. Although Internet of Things (IoT) devices are capable of offering intelligent services, the massive amounts of data they gather and analyze pose a severe security threat. Researchers have spent a lot of work building sophisticated NIDS to prevent the exploitation of IoT data in smart applications. However, present methods may be vulnerable to attack, particularly new attacks, due to a lack of sufficient and evenly distributed attack data used to train the detection strategy. This study introduces a original hierarchical adversarial attack (HAA) generation strategy, which in turn targets the convolutional neural network-based intrusion detection in IoT devices with a limited budget, and therefore realizes the level-aware black-box adversarial attack approach. To generate adversarial instances with little disturbance, an intelligent approach is developed that relies on a saliency map methodology. To do this, a shadow CNN framework has been constructed. Using a random walk having restart based hierarchical node selection approach, a subset of the targeted IoT network's nodes with the lowest structural strength are selected. The proposed HAA generation technique is evaluated in comparison to three reference methods using the publicly available data set UNSW-SOSR2019. In a comparison of the two most sophisticated convolutional neural network (CNN) designs, GCN and JK-Net, it was shown that the classification accuracy for NIDS in IoT settings may be reduced by more than 30%. © 2023 IEEE.

关键词： Network security

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Enhancing Multi-UAV Path Planning Efficiency with security through Integration of Priority-Based Ant Lion Optimization Algorithm 6

Enhancing Multi-UAV Path Planning Efficiency with Security t...

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6th international conference on Next Generation Data-Driven networks, NGDN 2024

作者： Guan, Yunchong Sturdy So, Wei Zhi Peng, Su Zhang, Yihang Zhang, Yuanhao Wang, Yang Hakim, Fahreza Prima Abdullah, Mumtaz Zain Shenyang Aerospace University School of Computer Science Liaoning Shenyang China Haikou China

ISBN: (纸本)9798350388374

The research endeavors to revolutionize Multi-Robot systems (MRS) through a cutting-edge hybrid strategy, amalgamating Priority-Based Algorithms (PBA) and Ant Lion Optimization (ALO) algorithms. This innovative approach aims to elevate path planning efficacy and coordination within dynamic and intricate environments. Leveraging PBA for collision-free path prioritization and ALO for agile and dynamic decision-making, the methodology promises to significantly enhance MRS's operational efficiency in real-world scenarios. The proposed Hybrid of Asynchronous Centralized and Decentralized (HACD) algorithm introduces a pioneering framework for fostering robot collaboration and resolving conflicts via distributed leader strategies. By employing grid-based decomposition and leader election mechanisms, it ensures seamless navigation in multi-robot setups by segmenting paths and imposing conflict limits. Integrating prioritized planning with trajectory optimization using ALO emulates the efficiency of ant lions' hunting behaviors, yielding adaptive solutions for dynamic operational landscapes. In addition, adding the help of Secret Cryptographic Key as the compartment of SSL for the proposed algorithm. MATLAB simulations validate the algorithm's scalability, efficiency, and effectiveness across diverse robot configurations, underscoring its potential for applications spanning search and rescue operations, military maneuvers, and warehouse logistics, where optimized Multi-Robot Path Planning (MRPP) is paramount. © 2024 IEEE.

关键词： Multipurpose robots

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Usability Comparison of Level E-Commerce Platform in Indonesia: A Case Study of Tokopedia, Bukalapak, and Shopee 1

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3rd international conference on Smart Computing and Cyber security—Strategic Foresight, security Challenges and Innovation, SMARTCYBER 2023

作者： Rosa, Sri Listia Kadir, Evizal Abdul Apriyani, Cici Evizal, Muhammad Fikri Department of Informatics Engineering Universitas Islam Riau Riau Pekanbaru28284 Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien County974 Taiwan

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

E-commerce is a common platform for trading in this era and used online platform where buyer and seller have met the transaction. Usability is the process of optimizing the interaction between the user and the system, which can be done interactively, so that the user gets the correct information or better performs a function in the application. This research aims to analyze how is the usability of e-commerce platform in Indonesia, the most favorite and bigger platform selected for the case study. The comparison of e-commerce usability by distributes an online questionnaire to the user with more than 200 respondents in order to obtain data. The questionnaire was distributed using the likers and comment scale method, namely respondents were asked to complete a questionnaire that required them to indicate their level of agreement with a series of statements. The researcher analyzed and graphed the results obtained from the questionnaire data and tested whether the data was valid or not using the Python programming language, as well as recapitulating the data using Microsoft Excel. Then there are problems in this research, namely what applications provide more convenience to users, what applications are more efficient, and what applications provide better satisfaction to their users. In this work of the level of usability, take case on Tokopedia, Bukalapak and Shopee e-commerce platforms. Results show these three companies of e-commerce platform and the calculations achieve that the usability of application is in higher out of all attributes is Shopee which reaches up to 80% compare to others e-commerce. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic commerce

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