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An Efficient Quantum cryptanalysis of lightweight hash function - PHOTON-Beetle 2

An Efficient Quantum cryptanalysis of lightweight hash funct...

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2nd International Conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Allu, Swamy Naidu Tentu, Appala Naidu Kancharla, Gangadhara Rao Dep. of Computer Science & Engineering Acharya Nagarjuna University Andhra Pradesh Guntur522510 India CR Rao AIMSCS UoH Campus Telangana Hyderabad500046 India CR Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad500046 India

ISBN: (纸本)9798350340235

Quantum cryptanalysis methods are used to crack the majority of symmetric key ciphers. With this knowledge, cryptologists should be able to create post-quantum symmetric ciphers by incorporating these new quantum cryptanalysis techniques into their toolkit. Secure communication is crucial for applications related to the Internet of Things (IoT) to avoid cyberattacks. Cryptographic hash functions can be used to protect data integrity. The development of large-scale, fault-tolerant quantum computers is the most significant challenge. Recent techniques to attack symmetric key schemes can be achieved by using the Pre-image attack method, Simon’s algorithm, or by combining Simon’s and Grover’s algorithms. In this study, we explored some of the most recent methods for quantum cryptanalysis of symmetric key ciphers. Firstly, we discussed the design of quantum cryptanalysis methods, along with the advantages over classical procedures. In this paper, we illustrate the implementation of the NIST-standard lightweight hash function PHOTON-Beetle on a Quantum computer (using IBM Qiskit), which is mostly used in IoT devices. Efficient quantum implementation of PHOTON-Beetle is proposed in this paper. This paper calculates the quantum cost of PHOTON-Beetle for every layer and compares it with [1]. © 2023 IEEE.

关键词： IoT PHOTON-Beetle Post-quantum cryptography Quantum algorithms Quantum cryptanalysis Symmetric cryptanalysis

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Towards Unveiling Vulnerabilities and Securing IoT Devices: An Ontology-Based Approach

Towards Unveiling Vulnerabilities and Securing IoT Devices: ...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter TU Wien Institute of Computer Engineering Vienna Austria Dep. of Integrated Sensor Systems TU Wien Institute of Computer Technology Danube University Krems Austria

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In the rapidly expanding landscape of Internet of Things (IoT), sensors have emerged as pivotal components, playing a critical role in sensing and data collection. However, this crucial function also renders them susceptible to potential threats such as unauthorized access and security breaches. This paper investigates the vulnerabilities that lead to these threats and offers a comprehensive set of best practices aimed at fortifying the security of IoT devices. We highlight key measures such as secure device design, robust authentication mechanisms, encrypted communication protocols, regular updates, and emphasize the importance of collaborative efforts among stakeholders using an ontological approach. An Ontology provides a structured framework for organizing knowledge, facilitating clearer communication, efficient data management, and enhanced decision-making. Thus, this paper contributes to the development of a more secure and resilient IoT ecosystem.

关键词： Protocols Ecosystems Collaboration Authentication Ontologies Sensors Internet of Things Stakeholders Cryptography Best practices

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From Manual to Semi-Automated Safety and Security Requirements engineering: Ensuring Compliance in Industry 4.0

From Manual to Semi-Automated Safety and Security Requiremen...

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Annual Conference of Industrial Electronics Society

作者： Mukund Bhole Wolfgang Kastner Thilo Sauter Institute of Computer Engineering TU Wien Vienna Austria Dep. of Integrated Sensor Systems Institute of Computer Technology TU Wien Danube University Krems Austria

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In response to the growing need for compliance with diverse safety and security regulations, crucial for safeguarding both humans and machines in the era of Industry 4.0 (I4.0), it has also become increasingly crucial for industries to ensure adherence to safety and security standards. However, navigating the complexities of regulations and standards can be daunting, often leading to inefficiencies in compliance processes. Our goal is to foster a comprehensive understanding of an effective approach to meeting compliance requirements in these related fields. Therefore, this paper introduces a semi-automated method for ensuring compliance with unified safety and security standards within the context of I4.0. This approach empowers management teams to thoroughly analyze the specific requirements necessary to maintain a protected environment. Compliance can be achieved at both the asset component and system levels, aligning with the relevant standards. Additionally, we present a detailed analysis of a use case and its output, demonstrating the practical application and efficacy of our approach.

关键词： Industries Accuracy Standards organizations Manuals Regulation Safety Fourth Industrial Revolution Complexity theory Security Certification

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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds

AI-driven Risk Assessment and Decision-making in Healthcare ...

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Computational Intelligence and Network systems (CINS), International Conference on

作者： Abdelaziz Darwiesh Mohamed Elhoseny A.H. El-Baz Reem Atassi Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info. University of Sharjah Sharjah UAE Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai UAE

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

This study presents an AI-driven strategy for enhancing decision-making in healthcare applications by assessing and managing potential risks through the analysis of customer interactions on social media platforms, specifically Twitter. We propose the use of machine learning models to outperform traditional lexical approaches, addressing the complexities inherent in risk detection. Natural language processing methods are employed to efficiently process and analyze textual data, enabling healthcare institutions to make informed decisions about risk management. By leveraging customer feedback from the X platform, our approach aids in the development of effective healthcare programs by identifying and classifying risks. We create three specialized datasets for risk analysis, risk assessment, and hazard classification to optimize the training and dep.oyment phases. In our case study, which focuses on tweets related to healthcare for US consumers, we find that over 50% of the sample identifies potential risks, with nearly 80% indicating medium levels of risk. Commonly identified risks include financial, operational, and strategic threats. The proposed methodology demonstrates an average accuracy of 82.66% in risk detection, identification, and assessment, confirming its effectiveness in supporting AI-driven decision-making in healthcare application development.

关键词： Training Accuracy Social networking (online) Computational modeling Decision making Blogs Medical services Natural language processing Complexity theory Risk management

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IT Security Solutions for IT/OT Integration: Identifying Gaps and Opportunities

IT Security Solutions for IT/OT Integration: Identifying Gap...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In the contemporary landscape of convergence between Information Technology (IT) and Operational Technology (OT), maintaining asset visibility within the Industrial Control systems (ICS) infrastructure is paramount. Yet, the escalating targeting of legacy systems and air-gapped networks by cyber threats underscores the need for robust security measures and solutions to counter unauthorized access and malicious activities. This paper presents an analysis aimed at identifying gaps and opportunities in integrating IT Security Solutions with OT environments. These solutions offer comprehensive features such as real-time threat detection, asset identification, network monitoring, and incident response capabilities tailored specifically for the unique challenges posed by industrial control networks. Leveraging the Festo MPS 403–1 system as a case study, we delve into the intricacies of IT/ OT integration and highlight areas necessitating improvement to achieve seamless convergence and harness the advantages of unified systems across industries.

关键词： Industries Industrial control Security management Logic gates Software Threat assessment Security Monitoring Industrial Internet of Things Convergence

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Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

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Biomedical engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

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Machine learning algorithms in Bigdata Analysis and its applications: A review 4

Machine learning algorithms in Bigdata Analysis and its appl...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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Development of an Electricity Loop Current Monitor System Based on MQTT Broker

Development of an Electricity Loop Current Monitor System Ba...

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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chih-Cheng National Chi Nan University Dep. of Electrical Engineering Taiwan National Quemoy University Dep. of Industrial Engineering and Management Taiwan Providence University Dep. of Computer Science and Communication Engineering Taiwan National Central University Dep. of Information Management Taiwan

ISBN: (纸本)9781665437554

The energy monitoring system is always a critical and popular issue for a long time, although many information systems have been built and dep.oyed into schools, factories, schools, commercial organizations, and governments. For many years, most of them have closed systems based on their business considerations, so add-on sensor types or heterogeneous hardware and software integrations are the bottlenecks. The study is carried out to integrate the internet of thing (IoT) and message queue telemetry transport (MQTT) to design a mechanism and to implement a prototyping clouding platform as the proposed system to develop the AC collectors with electricity loop current sensors connected to the proposed controller with Ameba 8195 AM in the study. The AC collectors are dep.oyed in labs and classrooms in the National Chi Nan University and classrooms in Xinhe Elementary School to validate the usability and feasibility of the proposed controllers and the prototyping web system. A distributed message-deliver is developed as a publisher based on MQTT Broker to implement a central message-collector as a subscriber to transmit all data into the proposed system. The analytical and visualized reports are created for users and administrators in the proposed system to offer useful and real-time information as feedback to be the proof of concept (PoC). © 2021 IEEE.

关键词： Internet of things

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Evaluating the Impact of Injected Mobility Data on Measuring Data Coverage in CrowdSensing Scenarios

Evaluating the Impact of Injected Mobility Data on Measuring...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Alexander Kocian Michele Girolami Stefano Capoccia Luca Foschini Stefano Chessa Dep. of Computer Science University of Pisa Italian National Council of Research ISTI-CNR Pisa Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker’s profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.

关键词： Data privacy Crowdsensing Prevention and mitigation Distributed databases Probability Data models Security Proposals Mobile computing Anomaly detection

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Maximize Leader Election Algorithm Performance in Bi-Directional Ring Networks With Both Links Down Between Two Nodes

Maximize Leader Election Algorithm Performance in Bi-Directi...

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Information Technology (ACIT)

作者： Mohammed N. Al Refai Hamed Fawareh Hamzeh Aljawawdeh Fuad Sameh Alshraiedeh Mohammad Al-quraan Hisham Al-Odat Ahmed Ali Otoom Zeyad M. Jamhawi Mohammad Kanan Software Engineering Dep. Zarqa University Zarqa Jordan Computer Science Zarqa University Zarqa Jordan Department of Cyber Security Applied Science Private University Amman Jordan Department of Computer Science Applied Science Private University Amman Jordan Dep. of Industry Engineering University of Business and Technelogy Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The process of selecting a leader to avoid instability in distributed system network after leader failure is called Leader Election Algorithm (LEA). This process becomes more complicated when it faces intermittent or complete links disconnected. the network becomes a one-way behavior if both lines of communication between two points are disrupted and thus the network becomes very slow, especially in systems where time is a critical issue, and the process of communication between points becomes much slower if the network leader is next to the fault or close to it, so the criteria will be established to ensures that the system leader in the Bidirectional ring communication network is in a place that allows next leader to communicate with all points as quickly as possible, and this will be during the election *** research presents a solution for leader failure problem in bidirectional ring network. This research takes into account the existence of intermittent or disconnection links between nodes. It will produce three algorithms to determine a new leader in the network with maximum network performance. It uses the least probability of time steps and the number of messages needed to elect the new leader in bidirectional ring network with N communicated nodes. so that the system remains able to work and communicate with its best capacity during this problem with the number of message complexity O(N) in the worst case. The third scenario minimizes the value of proper leader ID $\frac{{I{D_{Current}}}}{{{I_{Current}}idStepCount}}$, and maximizes the value of proper leader place N − (N − nStepCount).

关键词： Voting Bidirectional control Complexity theory Communication networks Information technology Faces

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