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Exploring Quantum Computing Use Cases for Critical National Infrastructures KFUPM - College of computer Science & Engineering 7

Exploring Quantum Computing Use Cases for Critical National ...

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7th international conference on Future networks and distributed systems, ICFNDS 2023

作者： Alghamdi, Maryah Alghamdi, Sara King Fahd University Of Petroleum And Minerals Saudi Arabia Aramco Cyberani Saudi Arabia

ISBN: (纸本)9798400709036

Quantum computing is one of the evolving fields within computer science. It leverages benefits from physics, mathematics, and quantum principles in order to develop a computer with capabilities not ever witnessed before. Such a computer is based on complex physics and is able to store and process data in units of "qubits"that enable electronics or photons to be available in more than one state simultaneously. A distinguishing characteristic of a quantum computer is its ability to perform certain calculations and solve or simulate complex problems exponentially faster than classical computers. From a governmental perspective, such a technology is a double-edged sword. A quantum computer that is able to break public-key cryptography may pose threats due to its potential to leak confidential and sensitive government information. On the other hand, quantum cryptography and quantum key distribution can lead to a secured communication where exchanged data are secured against cyber-attacks. Despite the absence of a fully realized quantum computer model, governments worldwide recognize the substantial potential of quantum technology to drive economic development and safeguard critical national security data, they have actively positioned themselves in the race to advance quantum technology. This survey reviews the research and development of several branches of quantum computing used for critical national infrastructures. It aims to map potential use cases of quantum computing with critical national infrastructure applications. The survey is divided into two main sections, namely, quantum communication and cryptography, and quantum networks, where each of these is further reviewed from different angles. This includes opportunities and challenges quantum computing can pose for governmental communications. Also, it reviews the advancement of quantum satellites and quantum drones for establishing a reliable quantum network. In addition, it touches on the use of quantum technol

关键词： Quantum cryptography

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Integrity, Resilience and security of 5G Transport networks Based on SDN/NFV Technologies 24th

Integrity, Resilience and Security of 5G Transport Networks ...

引用

24th international conference on distributed computer and Communication networks, DCCN 2021

作者： Buzhin, I. Bessonov, M. Mironov, Y. Farkhadov, M.P. Moscow Technical University of Communications and Informatics Moscow Russia V.A. Trapeznikov Institute for Control Sciences of Russian Academy of Science Moscow Russia

ISBN: (纸本)9783030971090

5G networks are being actively implemented in many countries around the world. The goal of introducing technologies that make up the new standard is to reduce information transmission delays, increase transmission speed, and increase the number of devices served. To achieve the targets, 5G networks must be built using software-defined networking (SDN) and network function virtualization (NFV) technology. This paper analyzes the integrity, resilience and security of 5G networks based on SDN/NFV, as well as provides recommendations for the comprehensive information security of such networks. © 2022, Springer Nature Switzerland AG.

关键词： 5G mobile communication systems

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A Survey on Vehicular Ad hoc networks security Attacks and Countermeasures 6

A Survey on Vehicular Ad hoc Networks Security Attacks and C...

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6th international conference on Future networks and distributed systems, ICFNDS 2022

作者： Abuarqoub, Abdelrahman Alzu'Bi, Ahmad Hammoudeh, Mohammad Ahmad, Arslan Al-Shargabi, Bassam Cardiff School of Technologies Cardiff Metropolitan University Wales Cardiff United Kingdom Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Faculty of Information Technology Middle East University Amman Jordan

ISBN: (纸本)9781450399050

Vehicular Ad hoc networks (VANETs), a subcategory of Mobile ad hoc networks (MANETs), is a promising approach that is now evolving, gaining attention and momentum. VANET consists of highly mobile nodes and roadside infrastructure to communicate essential information across a network, such as collisions, traffic congestion, and vehicular destinations. Due to its frequent vehicle movement, time-critical response and hybrid architecture, it comes with a plethora of network security and privacy issues ranging from the potential of malicious attacks to the availability of essential information across the network and its overall integrity. There is much research devoted to enabling secure communication in VANETs for the best Intelligent Transport System (ITS) experience. In this paper, we address and discuss the characteristics and security issues including the existing VANETs attacks and preventive approaches to each security attack emphasising the main features and the major drawbacks. The review of these different countermeasures shows their strengths and weaknesses and how they can be implemented independently or in a hybrid model best suited to the situation. © 2022 ACM.

关键词： Vehicular ad hoc networks

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Enhancing security Features in Wireless Sensor networks using AI based Deep Learning Methods

Enhancing Security Features in Wireless Sensor Networks usin...

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

作者： Muhammad Puzhakkalaveettil M. Praveena Department of Computer Science Dr.S.N.S Rajalakshmi College of Arts and Science Coimbatore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Wireless sensor networks (WSNs) are crucial for a wide range of applications. However, their open architecture and limited resources make them vulnerable to intrusion. Improve the security of WSNs by incorporating advanced deep learning methods, including RNNs, Autoencoders, and Convolutional Neural networks (CNNs). When it comes to identifying intrusions, CNNs need to analyze spatial patterns in data, while RNNs are responsible for managing temporal relationships in sequential data. Autoencoders are commonly used in anomaly detection to identify typical data patterns. By training on preprocessed sensor data, these deep learning models ensure network availability and data integrity. They enable the immediate detection and handling of potential dangers. Evaluation metrics denote that in simulated assault scenarios, CNNs, RNNs, and autoencoders have achieved impressive accuracy rates of 95.2%, 97.8%, and 97.5%, respectively which showcases their effectiveness when compared to previous techniques. The proposed design emphasizes adaptability and resilience, offering WSNs robust protection against emerging cyber threats.

关键词： Deep learning Wireless sensor networks Adaptation models Accuracy Autoencoders Real-time systems Convolutional neural networks Protection Anomaly detection Resilience

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Novel Cyber Incident Management System for 5G-based Critical Infrastructures 12

Novel Cyber Incident Management System for 5G-based Critical...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Polozhentsev, Artem Gnatyuk, Sergiy Berdibayev, Rat Sydorenko, Viktoriia Zhyharevych, Oksana National Aviation University Kyiv Ukraine Yessenov University Aktau Kazakhstan State Scientific and Research Institute of Cybersecurity Technologies and Information Protection Kyiv Ukraine Almaty University of Power Energy and Telecommunication Almaty Kazakhstan Lesya Ukrainka Volyn National University Lutsk Ukraine

ISBN: (纸本)9798350358056

Modern critical infrastructure with diverse systems requires a security information and event management (SIEM) system for unified monitoring against cyber threats. This system collects log data, performs real-time analysis, flags threats, triggers alerts, and advises response strategies. Enhanced by AI, Internet of Things, and cloud technologies, modern SIEM systems have significantly improved and optimized threat detection. This research examines the functionality, basic operation, and comparative capabilities of current SIEM systems. In addition, a universal event correlation and cybersecurity incident management system was designed and studied specifically for 5G networks. Hybrid security data storage models were also developed to ensure fast search, scale with data volume, and interface with external storage. The research also formulated models for distributed data bus operation, which enables fast processing of large data streams with minimal latency and high resilience. The proposed system addresses key cybersecurity challenges and meets global standards for establishing cyber incident management systems in 5G-based critical infrastructure. © 2023 IEEE.

关键词： 5G-based critical infrastrucute cyber security cyber threats SIEM

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Lightweight Collaborative Inferencing for Real-Time Intrusion Detection in IoT networks 19

Lightweight Collaborative Inferencing for Real-Time Intrusio...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Morales, Gabriel A. Xu, Jingye Zhu, Dakai Slavin, Rocky The University of Texas at San Antonio Department of Computer Science San AntonioTX United States

ISBN: (纸本)9798350346558

The security in Internet-of-Things (IoT) networks becomes increasingly important with the growing popularity of IoT devices and their wide applications (e.g., critical infrastructure monitoring). However, traditional intrusion detection systems (IDS) are not suitable for IoT networks due to their large resource requirements. Moreover, IoT networks tend to have multiple access points for IoT devices and thus benefit from a distributed framework to enable collaborative prevention of potential attacks. To this end, we propose a lightweight collaborative distributed network IDS (NIDS) based on widely-utilized machine learning (ML) models, which are trained through a federated learning framework with two known datasets. We evaluate the distributed NIDS using the trained ML models on an IoT network testbed under seven types of attacks in comparison with Snort (a state-of-the-art IDS) and a centralized implementation of our proposed NIDS. An offline benchmark is also designed to measure the system's performance with regard to resource usage and response time. Our results show that the proposed distributed NIDS outperforms Snort in identifying malicious traffic and achieves a much lower false positive rate compared to the centralized version in real-time for all seven types of network attacks tested. © 2022 IEEE.

关键词： Network security

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DDoS Detection and Prediction Techniques-A Survey

DDoS Detection and Prediction Techniques-A Survey

引用

2023 international conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Goyal, Ashtha Matta, Priya Gautam, Pratiksha Graphic Era Deemed to Be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Hindustan College of Science and Technology Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350323917

The distributed Denial of service (D-DoS) attack continues to attract excess of assiduity in the cyber security world, owing to the tremendous damage it may bring to an organization's assets. Consequently, as internet consumer traffic and device access speeds rise substantially, it confronts a serious issue In Spite of having often bottlenecks, however, some approaches have devised mitigating strategies. Earlier, SDN was shown in order to protect enterprises against D-DoS cyber-attacks. D-DoS cyberattacks create server disruptions and monetary loss as a result of shutdowns as a result achieving Service level agreement (SLA) with customers continues to be a difficulty. This paper contains a complete literature study and a comprehensive study of the DDoS effect. Furthermore, it investigates the shortcomings of conventional DDoS detection and prediction systems in order to assist scientific and corporate researchers in building prominent DDoS detection and prediction methods. This review identified and referenced all journals with information related to the domain from multiple digital libraries, along with appropriate search strategies. However, it only involved a small number of journal papers and conference proceedings. These papers were cited in many studies and referred by the industry for real-time projects. © 2023 IEEE.

关键词： Neural networks

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Efficient Intrusion Detection Model based on LSTM and RNN

Efficient Intrusion Detection Model based on LSTM and RNN

引用

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

作者： C. Valarmathi S. John Justin Thangaraj Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Background: Consequently, there is an increase in network security vulnerabilities, making it difficult for network managers to protect their networks against all types of cyberattacks. Numerous methods for detecting network intrusions have also been created. However, the continual appearance of new vulnerabilities that current systems are unable to comprehend presents them with considerable ***: Introduce an improved Deep Learning (DL)-based network intrusion detection system (IDS), inspired by deep learning's remarkable capabilities in a variety of detection and recognition tasks. This paper investigates a hybrid deep learning model that combines Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM) networks to create a versatile and effective security detection system. This system is intended to detect and classify both known and unanticipated ***: The thorough simulation results show that the system achieves throughput rates of 96.6% and 97.7% for the NSL-KDD and KDDTest+ datasets, respectively. This study provides an efficient way for finding and avoiding intrusions, so effectively addressing security concerns. The outcomes of this kind of research facilitate the process of choosing the best algorithm that may be used to successfully detect upcoming cyberattacks.

关键词： Deep learning Cloud computing Ultrasonic imaging Clustering algorithms Classification algorithms Convolutional neural networks Internet of Things computer crime Artificial intelligence Long short term memory

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WiCHORD: A Chord Protocol Application on P2P LoRa Wireless Sensor networks 13

WiCHORD: A Chord Protocol Application on P2P LoRa Wireless S...

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13th international conference on Information, Intelligence, systems and Applications, IISA 2022

作者： Balatsouras, Christos-Panagiotis Karras, Aristeidis Karras, Christos Tsolis, Dimitrios Sioutas, Spyros Group Computer Engineering and Informatics Department Patras26504 Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

On the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for reliable wireless long range communications. LoRa is an emerging technology for effective long range communications which can be directly applied to IoT applications. Wireless sensor networks (WSNs) are by far an efficient infrastructure where sensors act as nodes and exchange information among themselves. distributed applications such as Peer-to-Peer (P2P) networks are inextricably linked with distributed Hash Tables (DHTs) whereabouts DHTs offer effective and speedy data indexing. A distributed Hash Table structure known as the Chord algorithm enables the lookup operation of nodes which is a major algorithmic function of P2P networks. In the context of this paper, the inner workings of Chord protocol are highlighted along with an introduced modified version of it for WSNs. Additionally, we adapt the proposed method on LoRa networks where sensors function as nodes. The outcomes of the proposed method are encouraging as per complexity and usability and future directions of this work include the deployment of the proposed method in a large scale environment, security enhancements and distributed join, leave and lookup operations. © 2022 IEEE.

关键词： Internet of things

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Federated Learning Approaches to Diverse Machine Learning Model: A Review 7th

Federated Learning Approaches to Diverse Machine Learning Mo...

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7th international conference on Information and Communication Technology for Intelligent systems, ICTIS 2023

作者： Sharma, Sunena Kumar, Sunil Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar125001 India

ISBN: (纸本)9789819937608

Federated learning is a distributed machine learning technique that allows several data owners to jointly train shared models without exchanging their confidentials data as the need for centralized. It has become increasingly popular in recent years as it provides privacy preservation, scalability to large dataset, and flexibility compared to traditional centralized approaches. Despite its advantages, federated learning also faces several challenges such as communication overhead, model staleness and non-IID data distributions. In this paper we present a survey study of the current research on federated learning techniques and challenges including system architectures, optimization algorithms, security protocols and privacy-preserving methods. Finally, it concludes with the discussion of potential future directions for research into federated learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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