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On Developing Secure distributed Sensor networks

On Developing Secure Distributed Sensor Networks

引用

2021 international conference on Electrotechnical Complexes and systems, ICOECS 2021

作者： Makhmutov, Amir Vulfin, Alexey Mironov, Konstantin Ufa State Aviation Technical University Department of Computer Technology and Information Security Ufa Russia Moscow Institute of Physics and Technology Center of Cognitive Modelling Moscow Russia

ISBN: (纸本)9781665407274

This paper is devoted to the problem of using traditional cryptographic algorithms on low-resource ATmega328P microcontrollers. The performance of microcontrollers was assessed during working with cryptographic algorithms and hash functions. Based on received results, recommendations have been developed for the developing of secure data transmission systems for industrial heterogeneous networks that combine devices with limited computing capabilities. © 2021 IEEE.

关键词： Heterogeneous networks

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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international conference on Parallel and distributed systems (ICPADS)

作者： Ran Qiu Shengrong Zhao Hu Liang 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

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images.

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Cybersecurity elements in remote payment systems: research models

Cybersecurity elements in remote payment systems: research m...

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2022 IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Elena, Smirnova Sergey, Skryl Artyom, Sychev Mikhail, Sychev Bauman Moscow State Technical University Computer systems and networks dept Moscow Russia Bauman Moscow State Technical University Information security dept Moscow Russia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

The article is devoted to the consideration of important elements of cybersecurity of payment systems for remote banking services. The existing technologies of customer service in the service of payment systems are analyzed. The features of computer attacks on remote payment systems are considered, vulnerable devices are described. Means and technologies for protecting these systems are proposed. A mathematical model is proposed as a tool for substantiating promising areas for improving the technologies for ensuring cybersecurity of the payment systems service. The protection of payment system information from computer attacks is considered based on the legal requirements of the Bank of Russia. The work has been supported by the Ministry of Science and Higher Education of the Russian Federation (Project #0705-2020-0041), by Russian Scientific Fund (Project # 22-11-00049), as well as by Governmental Program 'Universities 2030'. © 2022 IEEE.

关键词： Cybersecurity

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

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

引用

international conference on Software Defined systems (SDS)

作者： Paulo H. L. Rettore Philipp Zißner Marcio Lawisch Sean Kloth Edison Pignaton de Freitas Peter Sevenich Communication Systems (KOM) Fraunhofer FKIE Bonn Germany Federal University of Rio Grande do Sul Porto Alegre Brazil Institute of Computer Science 4 University of Bonn Bonn Germany

ISBN: (数字)9798331518325

ISBN: (纸本)9798331518332

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.

关键词： Feature extraction Denial-of-service attack Software Blockchains Software reliability security Software defined networking Monitoring Load flow control

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A Multi-Agent System Based on Dynamic Load Balancing for Collaborative Intrusion Detection

A Multi-Agent System Based on Dynamic Load Balancing for Col...

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international conference on Networking and Advanced systems (ICNAS)

作者： Nadia Hocine Chihabeddine Zitouni Department of computer sciences CSTL laboratory University of Mostagnem Mostaganem Algeria Faculty of Computing and Telecommunications Poznan University of Technology Poznan Poland

Intrusion detection systems contribute to the improvement of the security of systems as well as the confidentiality and integrity of data. These systems are based on traffic analysis, which makes their performance and response time sensitive to the density of network traffic and distributed denial of service attacks. In this paper, we suggest a new collaborative intrusion detection system based on a multi-agent system. The intrusion detection technique relies on dynamic load balancing of traffic analysis to enhance the performance of intrusion detection, especially distributed denial of service attacks. In particular, mobile agents collaborate to analyze traffic using decision trees, support vector machines, and neural networks. The results of the experiment, using NSL-KDD public dataset, show that the proposed intrusion detection system architecture improved the performance and the scalability of intrusion detection compared with state of the art techniques.

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Counter Denial of Service for Next-Generation networks within the Artificial Intelligence and Post-Quantum Era

Counter Denial of Service for Next-Generation Networks withi...

引用

IEEE international conference on Trust, Privacy and security in Intelligent systems and Applications (TPS-ISA)

作者： Saleh Darzi Attila A. Yavuz Computer Science & Engineering University of South Florida Tampa FL USA

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

Given the rise in cyber threats to networked systems, coupled with the proliferation of AI techniques and enhanced processing capabilities, Denial of Service (DoS) attacks are becoming increasingly sophisticated and easily executable. They target system availability, compromising entire systems without breaking underlying security protocols. Consequently, numerous studies have focused on preventing, detecting, and mitigating DoS attacks. However, state-of-the-art systematization efforts have limitations such as isolated DoS countermeasures, shortcomings of AI-based studies, and a lack of DoS integration features like privacy, anonymity, authentication, and transparency. Additionally, the emergence of quantum computers is a game changer for DoS from attack and defense perspectives, yet it has remained largely unexplored. This study aims to address these gaps by examining (counter)-DoS in the AI era while also considering post-quantum (PQ) security when it applies. We highlight the deficiencies in the current literature and provide insights into synergistic techniques to bridge these gaps. We explore AI mechanisms for DoS intrusion detection, evaluate cybersecurity properties in cutting-edge machine learning models, and analyze weaponized AI in the context of DoS. We also investigate collaborative and distributed counter-DoS frameworks via federated learning and blockchains. Finally, we assess proactive approaches such as honeypots, puzzles, and authentication schemes that can be integrated into next-generation network systems for DoS prevention and mitigation.

关键词： Bridges Privacy Analytical models Prevention and mitigation Weapons Authentication Collaboration Artificial intelligence Next generation networking Network systems

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Harnessing Machine Learning for Enhanced distributed Computing in Wireless Network systems

Harnessing Machine Learning for Enhanced Distributed Computi...

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Communication, computer Sciences and Engineering (IC3SE), international conference on

作者： K Rashmi R J Anandhi Navdeep Singh V Surya Pavan Kumar Krashnkant Gupta Ali Albawi Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Information Science Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Department of Electronics & Communication Engineering Amity University Greater Noida India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

A complete wireless network system for flexible resource sharing, spectrum management, security, and cross-layer optimization is described in this paper. Five algorithms comprise the system. These are ADS, QoS-ML, CLO, and DSM. Wireless networks adapt and grow, and these applications solve issues. Real-time resource allocation using predictive models enhances network performance, latency, and efficiency with the PRA algorithm. DSM uses adaptive spectrum utilization to reduce interference. ADS uses machine learning to detect unusual activity and implement flexible safety measures to make networks safer. QoS-ML improves routing options using real-time QoS data to ensure network-wide QoS. CLO alters layer connections, utilizes metrics across layers, and is continually searching for network improvements. A detailed analysis of the research examined each program's distinct qualities and how they function together. The discussion focused on how well the framework performed in various settings and was adaptable. In the conclusion, the algorithms' strengths and potential in distributed computing environments were highlighted. The recommended technique outperformed others in speed, latency, security, energy efficiency, scalability, and customer satisfaction. Performance trends, parameter distributions, and trade-offs were shown, making the structure more dependable. By offering a full, adaptive, and successful method, this study advances wireless network management. Optimizations, scalability, and real-world applications might be studied to ensure the system works in various conditions. In challenging and changing situations, the proposed architecture supports wireless network expansion.

关键词： Cross layer design Wireless networks Scalability Quality of service Routing Prediction algorithms Real-time systems

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FedSec-CPS: Federated Learning Based security Framework for security of Cyber-Physical systems 6

FedSec-CPS: Federated Learning Based Security Framework for ...

引用

6th international conference on Futuristic Trends in networks and Computing Technologies, FTNCT 2024

作者： War, Muhammed Rafeeq Singh, Yashwant Sheikh, Zakir Ahmad Department of Computer Science and Information Technology Central University of Jammu J and K Rahya Suchani Bagla181143 India

The proliferation of cyber-physical systems (CPS) has posed significant challenges in ensuring their security due to their distributed nature and resource constraints. Machine learning (ML) techniques have been utilized to detect known and zero-day attacks. However, traditional ML algorithms may exceed the computational requirements of CPS, leading to poor performance and efficiency. A potential strategy to enhance performance and efficiency of ML algorithms on constrained devices while also providing a more secure environment for data sharing is the use of Federated Learning (FL). This paper proposes a methodology for computationally efficient cyber security in constrained CPS using FL. The methodology utilizes Horizontal Federated Learning (HFL), and Vertical Federated Learning (VFL) with Random Forest (RF) as the base classifier. We evaluate the proposed methodology on TON-IoT (network traffic) dataset. Performance metrics of our model using RF with FL architecture ranged from 74% to 100%, while computation time was low. FL-based methodology achieved better performance and efficiency while preserving the privacy of CPS data. Moreover, this paper emphasizes the potential of FL to address the security and performance challenges of traditional ML algorithms in constrained CPS. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Federated learning

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Performance Analysis of Ensemble Learning for Botnet Detection in IoT

Performance Analysis of Ensemble Learning for Botnet Detecti...

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IEEE international conference on Communication, networks and Satellite (ComNetSat)

作者： Fatur Rahman Satria Mandala School of Computing Telkom University Bandung Indonesia Humic Engineering & School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The phenomenal rise of the Internet of Things (IoT) has connected a wide range of devices, from industrial systems to household appliances, revolutionizing daily life, yet this unprecedented growth has introduced significant cybersecurity challenges. One of the most pressing issues is the emergence of BotNets, which are networks of compromised Internet of Things devices used by hostile actors to launch huge attacks like distributed Denial of Service (DDoS). Because these BotNets exploit the often-weak security measures incorporated into Internet of Things devices, they represent a significant threat to global cybersecurity. This paper investigates the applicability of ensemble learning techniques, including Bagging, Boosting, and Stacking, for BotNet detection in Internet of Things contexts. Utilizing the BoTNeTIoT-L01 dataset, our comprehensive analysis reveals that both Bagging and Stacking, with their perfect accuracy rates of $\mathbf{1. 0 0}$ across all attack types, outperform the other methods, highlighting their potential as critical tools for enhancing IoT security. The implications of these findings are significant, providing a pathway for developing more robust cybersecurity strategies that can adapt to the evolving landscape of cyber threats. By focusing on advanced ensemble learning techniques, this research offers valuable insights into the future of IoT security, ensuring that these interconnected devices remain both innovative and secure.

关键词： Accuracy Satellites Botnet Stacking Pressing Boosting Internet of Things Ensemble learning computer crime Bagging

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Internet-of-Things Data security: Challenges, Countermeasures and Opportunities 23

Internet-of-Things Data Security: Challenges, Countermeasure...

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Proceedings of the 7th international conference on Future networks and distributed systems

作者： Farag Azzedin Ibraheem Al-Hejri Information and Computer Science Department King Fahd University of Petroleum and Minerals Saudi Arabia

ISBN: (纸本)9798400709036

Internet of Things (IoT) has introduced a revolutionary paradigm that unleashes multitude of functionalities and possibilities. Currently, IoT and its extensive range of applications have become the focal point of numerous research endeavors. While the inherent characteristics of IoT make it highly adaptable to real-world scenarios, they also expose it to potential cybersecurity risks. IoT has experienced an unprecedented number of cyberattacks, which not only affect businesses but also pose potential risks to human lives. Therefore, it is crucial to establish a comprehensive classification of IoT challenges and identify the available countermeasures. In this paper, we conduct an extensive survey on IoT security challenges and countermeasures, and propose a taxonomy to categorize them based on the three data states. Furthermore, we discuss future directions towards IoT data security.

关键词： Challenges

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