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Enhanced SNR Based Admission Control Algorithm for Vehicular Ad-Hoc Network 23

Enhanced SNR Based Admission Control Algorithm for Vehicular...

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

作者： Aws Zuhair Sameen Mohammed Hasan Alwan Mayssam Saad Kadhum Yousif I Hammadi Mokhalad Abdulameer Kadhim Alsaeedi Ahmed Aziz Mohammed Saleh Ali Muthanna Department of Medical Instrumentation Engineering Techniques Al-Farahidi University Iraq Department of Communications Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Institute of Computer Technologies and Information Security Southern Federal University Russia

ISBN: (纸本)9798400709036

Vehicular Ad-hoc Network (VANET) becomes a fundamental subcategory of mobile ad-hoc networks that provides vehicles to communicate with each other and with roadside infrastructure smartly. Various VANET performance challenges are considered in terms of Quality of Service (QoS) due to vehicle speed and transmission rates. Different problems cause performance degradation as performance anomaly where high rates of vehicles have to meet the speed of the low rates vehicle while transmitting data. In this paper, a QoS-aware Signal to Noise Ratio (SNR) admission control mechanism (QASAC) is proposed to handle the performance anomaly problem while maintaining the QoS levels for different traffic types. QASAC has been compared against the latest SNR based admission control mechanism. QASAC has enhanced the performance of data delivery up to 23% in terms of data dropping rates for high priority traffic.

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NPV: Fast Network Policy Verification for Cloud-Native Networking

NPV: Fast Network Policy Verification for Cloud-Native Netwo...

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international conference on distributed Computing systems

作者： Shunbin Dong Yumin Xie Jin Zhao Kun Qiu School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Network policy plays a crucial role in cloud-native networking, especially in multi-tenant scenarios. It provides precise control over connectivity by specifying source and destination endpoints, traffic types, and other criteria to allow or deny traffic. However, manual configuration of these policies introduces the risk of errors, leading to isolation violations or network service unavailability. Therefore, network policy verification is essential for maintaining security and quality of service in cloud-native networking. Currently, a naïve approach involves individually checking each policy within the cluster, which can take over 100s for verification in a cluster size of over 100k. Existing verification frameworks, like Kano and Verikube, improve performance by leveraging pre-filtering and Satisfiability Modulo Theories (SMT) solvers, achieving a 3.12x to 12.99x performance boost over the naïve baseline. However, as network policy changes rapidly within 100ms in real cloud-native networks, both frameworks need over 10s to perform verification for cluster sizes over 100k, which is far from satisfying. To overcome these issues, we propose and implement a novel network policy verification framework NPV, which utilizes the policy-label pre-filter process with bitwise compression. We further enhance the policy verification algorithm with a policy-namespace divide-and-conquer strategy to improve the data-level parallelism. We implement NPV on commodity servers and evaluate its performance using real network policy datasets. Our experiments indicate that, compared with the state-of-the-art methods, NPV can achieve up to 139.00x to 651.06x improvement in verification time compared to Kano and Verikube, with 65% less memory usage.

关键词： Memory management Clustering algorithms Quality of service Manuals Parallel processing Servers security

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An Efficient Hybrid Red Panda Optimization-Similarity-Navigated Graph Neural Network with Blockchain Technology for Shared Electric Vehicle Charging

An Efficient Hybrid Red Panda Optimization-Similarity-Naviga...

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Sustainable Expert systems (ICSES), international conference on

作者： Sammy. F Rajeshwari S Prashanth G K Babu Thirumalaimuthu Hemant Singh Pokhariya Natrayan L Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Mathematics Bangalore Institute of Technology Bengaluru Karnataka India Department of MCA Siddaganga Institute Technology Tumkur Karnataka India Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai Tamil Nandu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of EngineeringSIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Blockchain technology for shared electric vehicle (EV) charging makes use of a distributed ledger in handling different transactions which promotes authenticity in charging. However, this technology can be non-scalable at times which slows down the transaction rates as the scale increases in an organization and may also demand high computational as well as energy requirements which increases the inefficiency of the total system, the operating expenses of EV charging station, costs of energy utilized for charging, cost of maintenance and cost of station infrastructure may prove to be a weakness, impacting the feasibility of infrastructure deployment and upkeep expenses of the charging stations. To overcome these drawbacks, this paper proposes a hybrid method for blockchain technology with shared EV charging. The proposed approach combines the use of the Red Panda Optimization (RPO) with Similarity-Navigated Graph Neural Network (SNGNN). Hence, it is named as hybrid RPO-SNGNN approach. Reducing the operating cost of EV charging position and maximizes the security efficiency is the main aim of the proposed approach. To optimize scheduling and resource allocation by finding efficient paths by RPO and predict future demand and user behaviour patterns by SNGNN. By then, the proposed model has been put into practice on the MATLAB work platform and contrasted with other existing methods. The proposed technique outperforms all existing techniques, including Fuzzy-based Cross Hierarchical Reinforcement Q Learning (FCHRQL), Non-Dominated Sorting Genetic Algorithm-Long Short-Term Memory Network (NSGA-II-LSTMN), and Ant Colony Optimization-Proximal Policy Optimization (ACO-PPO). The proposed method shows the operating cost of $550 and the security rate is 97%. From the result, the proposed approach operating cost is low and the security rate is high when compared to the existing approaches.

关键词： Costs Q-learning Electric vehicle charging Graph neural networks Scheduling Blockchains security Resource management Optimization Sorting

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A Study on MANET: Along with Recent Trends, Applications, Types, Protocols, Goals, Challenges

A Study on MANET: Along with Recent Trends, Applications, Ty...

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Intelligent Computing and Research Trends (ICRT), international conference on

作者： Deepak Arya Pramod Mehra Parag Jain Abhay Bhatia Computer Science & Engineering Roorkee Institute of Technology Roorkee India

The (MANET) ad-hoc mobile network is an automated, network in which android systems link to wireless devices. Ad hoc word means "for this purpose". In MANET any device can easily move in various directions. MANETs (Mobile Ad Hoc networks) are the basics of broadband networks. In MANET network user can link and get connected anywhere, anytime through communication. MANET introduces a new co system for communication. MANET does not require a fixed structure relies on unconnected ports for service transportation delivery. The temporary hoc network is not dependent on mobile devices (android phones, laptop, PCs, sensors, etc.) that connect exclusively to wireless connectors. Ad-hoc work on a distributed system to provide the required network performance when there is no fixed structure. In this network, it acts as a standalone network or with many attachment points on mobile networks. This paper provides view on the features, applications of ad hoc networks, characteristics and various attacks and challenges faced by programmers and Users . We analyze MANET security objectives and will outline the features, structure, types and challenges of research and the latest trend analysis presented on Mobile Ad Hoc Network to explore open-ended issues in developing navigation strategies for MANET.

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Resource Allocation in Cloud Computing Using Genetic Algorithm and Neural Network

Resource Allocation in Cloud Computing Using Genetic Algorit...

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IEEE international conference on Smart Cloud (SmartCloud)

作者： Mahdi Manavi Yunpeng Zhang Guoning Chen Department of Computer Science University of Houston Houston USA Department of Information Science Technology University of Houston Houston USA

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while reducing idle becomes more and more challenging. Efficient cloud-based scheduling is also highly sought by modern transportation systems to improve their security. In this paper, we propose a hybrid algorithm that leverages genetic algorithms and neural networks to improve scheduling. Our method classifies tasks with the Neural Network Task Classification (N2TC) and sends the selected tasks to the Genetic Algorithm Task Assignment (GATA) to allocate resources. It is fairness aware to prevent starvation and considers the execution time, response time, cost, and system efficiency. Evaluations show that our approach outperforms the state-of-the-art method by 3.2 % at execution time, 13.3 % in costs, and 12.1 % at response time.

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DCNN-based load balancing and computation offloading of Intelligent Video Surveillance with Edge Computing

DCNN-based load balancing and computation offloading of Inte...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), international conference on

作者： Shweta Koparde G. Sujatha Srinivas Jangirala P. Santhosh Kumar Computer Engineering Dr. DY Patil Institute of Technology Pune India Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Ghatkeshar India Jindal Global Business School O.P. Jindal Global University Sonipat India Information Technology College of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

In this study, we offer a distributed Intelligent Video Surveillance (DIVS) system that is set up in an environment of edge computing and is based on Deep Learning (DL). For the DIVS system, we developed a distributed DL training model and a multi-layer edge computing architecture. In order to lower the high network connection overhead and offer precise and low-latency video analysis solutions, the DIVS system may move computing processes from the network core to the network edges. We implement the proposed DIVS system to address the workload balance, model synchronization, and concurrent training problems. We offer task-level parallel and model-level parallel training approaches to further accelerate the video analysis process. Deep Convolutional Neural networks with Load Balancing and Computation Offloading (DCNN-LBCO) is a technique for creating a scalable cloud-based online video platform. In order to minimize security concerns, we provide a particular security layer for MEC systems in this study along with a load balancing and computation offloading technique that work together. A load balancing and computation offloading (LBCO) technique is first recommended in order to distribute MDU among sBSs effectively. A specific advanced encryption standard (AES) cryptographic method is also provided, together with an encryption and decryption key based on electrocardiogram (ECG) signals, to prevent data vulnerability during transmission. A load balancing, compute offloading, and security model that is integrated is provided to lessen the system's time and energy requirements. Extensive testing results show that, as compared to local execution, our technique may save between 68.2% and 72.4% of system utilisation, both with and without extra security layers.

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Machine Learning Approach for Mitigating security Threats in IoT Environment

Machine Learning Approach for Mitigating Security Threats in...

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international conference on Inventive Research in Computing Applications (ICIRCA)

作者： Harnit Saini Saroj Bala S.Jhansi Ida K. Suresh Kumar Shankerpally Swetha Divyabarathi P Department of CSE Ajay Kumar Garg Engineering College Ghaziabad Department of MCA Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department of CSE Kongu Engineering College Erode Department of Information Technology CVR College of Engineering Telangana Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection System (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated.

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A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog systems

A Secure Deep Q-Reinforcement Learning Framework for Network...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Sujit Bebortta Subhranshu Sekhar Tripathy Vandana Sharma Jnana Ranjan Behera Aishwarya Nayak Department of Computer Science Ravenshaw University Cuttack India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Computational Sciences CHRIST (Deemed to be University) Delhi NCR Campus India School of Electronics KIIT Deemed to be University Bhubaneswar India Department of Computer Sceince & Engineering DRIEMS University Cuttack India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes’ distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks

关键词： Technological innovation Accuracy Network intrusion detection Telecommunication traffic Real-time systems Malware Pattern recognition

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SECURE - MEDSHARE: Decentralized Patient Data Sharing for Improved Healthcare

SECURE - MEDSHARE: Decentralized Patient Data Sharing for Im...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), international conference on

作者： Vansh Kapoor Dhara Vadagasiya Revathy S. Department of computer science and engineering Hindustan Institute Of Technology and Science Chennai Tamil Nadu

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Blockchain provides a decentralized framework for peer-to-peer networks' data and transaction management. The basic approach to the proposed system is to create a trustworthy and secure platform that enables doctors to safely store, access, and manage patients' private medical information while abiding by stringent privacy laws to protect patient confidentiality. Data that has been separated into blocks can be distributed because of to the decentralized system. Then, every block is shielded against hackers trying to change the data. All of the network's peers, or miners, or users, must validate each modification to the data using modern cryptography techniques. The SECURE - MEDSHARE system uses blockchain technology to store patient data from different hospitals in a decentralized and secure way, overcoming the limitations of local servers. In an era where data privacy and security are paramount, this Proposed System presents a pioneering solution leveraging blockchain technology for secure medical data sharing. The system's foundation relies on the decentralized nature of blockchain, implemented through Ethereum and Hardhat for robust smart contract deployment and management. Interactions occur via a user-friendly React frontend, integrating seamlessly with MetaMask for secure blockchain transactions. The Proposed System utilizes IPFS for decentralized file storage, ensuring data integrity and accessibility. Smart contracts play a pivotal role, orchestrating access controls and establishing a transparent framework for healthcare data sharing. This innovative approach promises heightened security, privacy, and efficiency in managing sensitive medical information, catering to the evolving needs of modern health care systems. The system's architecture and functionality align with the growing demand for reliable, decentralized solutions in data-sensitive domains, positioning it as a potential cornerstone in revolutionizing healthcare data management

关键词： Privacy Hospitals Smart contracts Medical services Reliability engineering Blockchains Peer-to-peer computing

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Effectiveness Analysis Of Blockchain Mechanisms Using Consensus Algorithms

Effectiveness Analysis Of Blockchain Mechanisms Using Consen...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Munish Sabharwal Fayzullo Makhmadiyarovich Nazarov Bunyod Eshtemirov School of Computing Science & Engineering Galgotias University Samarkand State University

Recently, along with the development of information technology, blockchain technology has been developing all over the world. On the basis of blockchain technology, it will be possible to develop the digital economy, ensure information security, and increase reliability in data exchange. Due to the high potential of blockchain technology, it has led to the study of its wide application in various fields. These processes have led to the creation of blockchain-based systems for various purposes. Some blockchain mechanisms can also be used to increase data reliability in information systems that provide services in computer networks. However, at the same time, most of the blockchain-based systems have several disadvantages related to their performance and security. To solve these shortcomings, it is necessary to improve the security of blockchain-based systems. The main component of any blockchain-based system is its important consensus algorithm, which in many ways makes it possible to ensure the authenticity and security of information. Consensus algorithms serve to prove the authenticity of executed transactions. The application of consensus algorithms to the blockchain technology, depending on the functional task, allows to increase the reliability of the data. In this article, the effectiveness of using consensus algorithms based on blockchain mechanisms is studied.

关键词： computer network reliability Information security distributed information systems Consensus algorithm computer networks Blockchains Reliability

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