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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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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

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

ISBN: (纸本)9798350313413

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. © 2023 IEEE.

关键词： advanced encryption standard (AES) Deep Convolutional Neural networks (DCNN) Deep Learning (DL) distributed Intelligent Video Surveillance electrocardiogram (ECG)

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security Landscape of Anomaly based Defence Mechanisms in Edge Environments 9

Security Landscape of Anomaly based Defence Mechanisms in Ed...

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9th international conference on Smart Computing and Communications, ICSCC 2023

作者： Wath, Aditya Kurian, Tom Martin, John Paul Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350314090

Edge computing is a computational paradigm that brings computation closer to the end user. Even though real-time applications benefit from the extremely low response time of such a computing paradigm, the heterogeneity of end devices and the distributed nature of data in an edge computing environment falls prey to cyber attackers to exploit their vulnerabilities. Thus, security in edge computing is a great cause of concern that needs to be addressed. With the use of various Machine Learning(ML) and Deep Learning(DL) techniques, anomaly detection can assist in the real-time detection of security threats and operational concerns, enabling quick response. In this paper, we explore the current state of security in edge computing environments and some of the recent work done to safeguard the network using network intrusion detection systems(NIDS) with an extended focus on the use of Graph Neural networks(GNNs). Additionally we also discuss the robustness of the NIDS by invading them with the adversarial attacks which are designed so as to fail the existing installed NIDS on the network and investigate the defence mechanisms available to safeguard them. © 2023 IEEE.

关键词： Anomaly detection

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Hebbian Continual Representation Learning 56

Hebbian Continual Representation Learning

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56th Annual Hawaii international conference on System Sciences, HICSS 2023

作者： Morawiecki, Pawel Wolczyk, Maciej Krutsylo, Andrii Śmieja, Marek Polish Academy of Sciences Institute of Computer Science Poland Faculty of Mathematics and Computer Science Jagiellonian University Poland

ISBN: (纸本)9780998133164

Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the independent and identically distributed assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks. To reduce this performance gap, we investigate whether biologically inspired Hebbian learning is useful for tackling continual challenges. In particular, we highlight a realistic and often overlooked unsupervised setting where the learner has to build representations without any supervision. By combining sparse neural networks with the Hebbian learning principle, we build a simple yet effective alternative (HebbCL) to typical neural network models trained via gradient descent. Due to Hebbian learning, the network have easily interpretable weights, which might be essential in critical application such as security or healthcare. We demonstrate the efficacy of HebbCL in an unsupervised learning setting applied to the MNIST and Omniglot datasets. We also adapt the algorithm to the supervised scenario and obtain promising results in the class-incremental learning. © 2023 IEEE computer Society. All rights reserved.

关键词： Unsupervised learning

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The Combination of P-BFT and RAFT: A New Approach to Building networks that Provide Reliability and security 23rd

The Combination of P-BFT and RAFT: A New Approach to Buildin...

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23rd international conference on Computational Science and its Applications (ICCSA)

作者： Bogdanov, Alexander Shchegoleva, Nadezhda Khvatov, Valery Dik, Gennady Kiyamov, Jasur Dik, Aleksandr St Petersburg Univ St Petersburg Russia St Petersburg State Marine Tech Univ St Petersburg Russia DGT Technol AG Toronto ON Canada St Petersburg LLC Syst Technol St Petersburg Russia

ISBN: (纸本)9783031371042;9783031371059

The article considers two data processing algorithms in distributed systems: P-BFT and Raft. Both algorithms are designed to ensure data consistency and avoid conflicts in distributed environments. However, they have different approaches to solving these problems. The P-BFT algorithm operates at the bottom layer of data access and uses the principles of distributed consensus to achieve high transaction processing speed. Raft, in turn, operates at the top layer of data access and provides guarantees for data integrity and system state consistency. The article discusses the advantages and disadvantages of both algorithms and the choice between them depends on the requirements and characteristics of a particular system. Recommendations are given on the use of hybrid algorithms for more efficient operation of the system.

关键词： lockchain distributed system RAFT P-BFT

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A Mutual Intrusion Detection System for MANET to Address DDoS attacks using Data Mining Techniques 2

A Mutual Intrusion Detection System for MANET to Address DDo...

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2nd international conference on Sustainable Computing and Data Communication systems, ICSCDS 2023

作者： Bala, K. Renjith, P.N. Latha, M. Santhanalakshmi, S.T. School of Computing Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamilnadu Chennai600073 India Department of Computer Science and Engineering VIT Chennai Campus Tamilnadu Chennai600127 India Srm Institute of Science and Technology Department of Information Technology Ramapuram Campus Tamilnadu Chennai600089 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9781665491990

A type of wireless communication network called a mobile ad hoc network (MANET) is composed of numerous flexible components that can move in any of the network's many directions. The Intrusion Detection System (IDS) is a systematic tool implemented to spot suspicious activity with network malfunctions and sends an alert to the security control center. A malicious effort to interfere with the server's or networks regular operation is acknowledged as a distributed denial-of-service (DDoS). This occurrence causes the networking system's general performance to decline. Certain measures must be put into place to improve efficiency. These DDoS attacks are classified as application layer attacks, protocol attacks, and volumetric attacks in the networking system. The server experiences downtime as a result, and the network's general performance suffers. When resources are brought back into the network, it causes external tension. Data mining methods can be used to find this and fix it. © 2023 IEEE.

关键词： Mobile ad hoc networks

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A Review on Privacy Preservation in Cloud Computing and Recent Trends 7th

A Review on Privacy Preservation in Cloud Computing and Rece...

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7th international conference on Microelectronics and Telecommunication Engineering, ICMETE 2023

作者： Swain, Srutipragyan Pattnaik, Prasant Kumar Dash, Banchhanidhi School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9789819995615

Cloud computing is a distributed computing architectural model that offers on-demand computing services via the Internet. It includes servers, computing resources, applications, data storage, development tools, etc. However, despite the different services and benefits offered by the cloud, security and privacy are the primary hindrances for organizations for opting cloud. The real benefit of the cloud can be only enjoyed if the sensitive data kept in the database can be secured from unauthorized access. Privacy preservation in cloud computing is nothing but hiding sensitive data, where the data is stored in the software and databases scattered around the Internet. Various methods have been put forward by researchers to preserve privacy in cloud computing over the past few years. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensitive data

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Securing D2D Therapeutic Hiking Group in 5G networks for Partial Coverage Scenario 31

Securing D2D Therapeutic Hiking Group in 5G Networks for Par...

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31st international conference on Software, Telecommunications and computer networks, SoftCOM 2023

作者： Zemmoudj, Salah Bermad, Nabila University of Bejaia Research Unit LaMOS Faculty of Exact Sciences Bejaia06000 Algeria

ISBN: (纸本)9798350301076

Therapeutic hiking is a popular recreational activity that promotes physical exercise, nature exposure, and mental well-being. The potential of Device-to-Device (D2D) communication in 5G networks to enhance group communications has garnered significant attention. This paper focuses on securing D2D therapeutic hiking group communications in 5G networks, particularly in partial coverage scenarios where conventional network infrastructure may be limited or unavailable. A robust key agreement protocol is proposed to address the security requirements of D2D communications within therapeutic hiking groups. The protocol ensures secure and efficient communication among group members, enabling secure message exchange and sharing of sensitive information in challenging network conditions. A Relay node is introduced as an intermediary among User Equipments (UEs) and the base station, facilitating secure communication using elliptic curve cryptography. The protocol incorporates secure and efficient techniques for key generation, distribution, and updating, enabling secure and efficient communication within the therapeutic hiking group. The distributed nature of the protocol allows for flexibility and adaptability in partial coverage scenarios. Simulations are conducted to evaluate the effectiveness of our proposed protocol. © 2023 University of Split, FESB.

关键词： 5G mobile communication systems

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Secure Authentication of IoT Devices Using Upgradable Smart Contract and Fog-Blockchain Technology 7th

Secure Authentication of IoT Devices Using Upgradable Smart ...

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7th international conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Dinde, Shital H. Shirgave, S.K. Department of Technology Shivaji University Maharashtra Kolhapur India Department of Computer Science and Engineering DKTE’s Textile and Engineering Institute Maharashtra Ichalkaranji India

ISBN: (纸本)9789819914135

There are many new challenges related to the security of the Internet of Things networks due to the rapid growth of the technology. One of the main challenges is device security and authentication. Due to their limited processing and storage capabilities, the Internet of Things devices are unable to protect themselves from different attacks. Passwords or predefined keys have drawbacks that limit their uses. With high trust, integrity, and transparency, distributed ledger technology has the ability to address these issues in IoT. The solution discussed in this paper is to use fog computing and blockchain technology to authenticate IoT devices using smart tokens generated by an upgradable smart contract. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Federated Learning in Malware Detection 28

Federated Learning in Malware Detection

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Serpanos, Dimitrios Xenos, Georgios University of Patras Computer Technology Institute and Press DIOPHANTUS Dept. of Electrical and Computer Engineering Patras Greece University of Patras Campus Computer Technology Institute and Press DIOPHANTUS Patras Greece

ISBN: (纸本)9798350339918

Malware detection constitutes a fundamental step in safe and secure computational systems, including industrial systems and the Internet of Things (IoT). Modern malware detection is based on machine learning methods that classify software samples as malware or benign, based on features that are extracted from the samples through static and/or dynamic analysis. State-of-the-art malware detection systems employ Deep Neural networks (DNNs) whose accuracy increases as more data are analyzed and exploited. However, organizations also have significant privacy constraints and concerns which limit the data that they share with centralized security providers or other organizations, despite the malware detection accuracy improvements that can be achieved with the aggregated data. In this paper we investigate the effectiveness of federated learning (FL) methods for developing and distributing aggregated DNNs among autonomous interconnected organizations. We analyze a solution where multiple organizations use independent malware analysis platforms as part of their security Operations Centers (SOCs) and train their own local DNN model on their own private data. Exploiting cross-silo FL, we combine these DNNs into a global one which is then distributed to all organizations, achieving the distribution of combined malware detection models using data from multiple sources without sample or feature sharing. We evaluate the approach using the EMBER benchmark dataset and demonstrate that our approach effectively reaches the same accuracy as the non-federated centralized DNN model, which is above 93%. © 2023 IEEE.

关键词： Deep neural networks

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Shaping the Future of Mobile Edge Computing with Innovative Architectures and Designs for Web3.0 over 5G/6G networks 13

Shaping the Future of Mobile Edge Computing with Innovative ...

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13th IEEE international conference on Communication systems and Network Technologies, CSNT 2024

作者： Malviya, Ashwini Lora, Chandra Prakash Kannagi, A. Atlas SkillTech University Department of UGDX Maharashtra Mumbai India Vivekananda Global University Department of Chemistry Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350305463

Web3.0 apps, with their emphasis on decentralization, real-time processing, and low latency, hold the key to MEC's bright future. This calls for an all-encompassing, future-oriented strategy in MEC architecture and design. The suggested technique is a multi-stage strategy for molding the future of MEC via cutting-edge architectures and designs for Web3.0 over 5G/6G networks. In the first stage, you'll examine the needs and goals of Web3.0 in detail. Real-time, lowlatency interactions are essential, as is familiarity with the dynamics of distributed apps and blockchain technology. In the second stage, we examine the current MEC setup, from edge server dispersion to data center density to network topology. The technique takes use of the low latency, high throughput, and huge device connection offered by 5G and impending 6G networks to guarantee preparedness for the future. The suggested technique is based on novel building layouts as its foundation. Latency, throughput, resource usage, scalability, and security compliance are only few of the metrics that may be evaluated to guarantee that the design is effective. The difficulties of actual deployment are also discussed;they include issues like where to put edge servers and how to scale resources according to demand. To ensure that the process remains in step with developing technologies and user preferences, it is recommended that improvements be made in a series of iterations. © 2024 IEEE.

关键词： Blockchain

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