Multitask Learning for Security and Privacy in IoV (Internet of Vehicles)

Buttar, Ahmed Mateen; Sajja, Guna Sekhar; Gour, Sanjeev; Naved, Mohd; William, P.

doi:10.1002/9781119871989.ch12

Cited by 20 publications

(3 citation statements)

References 23 publications

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“…В другой работе, ориентированной на сетевые данные [10], была предложена гибридная модель MTL, использующая контрастное обучение (contrastive learning), кластеризацию и классификатор на основе многоуровневого восприятия (Multiple Layer Perception, MLP). С точки зрения конкретного применения в специальных средах IoT, способность MTL создавать одну унифицированную первичную модель для решения нескольких задач с использованием одного и того же набора данных позволяет хорошо адаптироваться и работать в динамических в распределенных системах с обширными объемами данных [11,12].…”

Section: релевантные работыunclassified

“…Функция преобразования выполняет SoftMax при умножении входного сигнала x и соответствующей обучаемой матрицы параметров. Когда имеется n сетей, реализованных в общем блоке, а W gt обозначает обучаемые веса, процесс вычисления распределения по множеству моделей и создания взвешенной суммы выходных данных всех моделей представляется с помощью (5): В WeightedBCE инициализация весов потерь рассчитывается на основе основных меток истинности y. Задавая отрицательную метку как ноль, а положительную метку как единицу, начальные веса, соответствующие отрицательным случаям ratio neg и положительным случаям ratio pos , вычисляются как (10) и (11): ratio neg = Σ(y) / len(y), (…”

Section: (а) (б) рисунок 3 механизмы жесткого и мягкого совместного и...unclassified

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Detecting Attacks on the Internet of Things Based on Multitasking Learning and Hybrid Sampling Methods

2024

Cybersecurity Issues

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The purpose of the study: To analyze and implement methods of multi-task learning and hybrid sampling of network traffic data to detect attacks in Internet of Things networks in order to improve the representation of minority classes and achieve data balance; compare the performance of various neural networks based on single-task and multi-task learning with hard and soft separation of parameters; implement weight optimization methods that provide automatic initialization and tuning of deep learning parameters for attack detection tasks in Internet of Things networks.Research methods: system analysis, modeling, deep machine learning.Results obtained: An approach to detecting attacks in Internet of Things networks based on multi-task learning is proposed. A comparison was made of the effectiveness of single-task learning models and multi-task learning models with hard and soft sharing of parameters. A hybrid sampling method is presented that combines random undersampling with oversampling based on a generative adversarial network. In addition, a weight initialization algorithm is implemented to eliminate imbalanced classification in IoT networks, ensuring high performance of the model for different classes of attacks represented in the dataset. Experiments were performed on different datasets, and the results showed that multi-task learning models outperform single-task learning for network traffic classification, achieving higher detection performance, especially for rare attacks.Scientific novelty: A new approach to detecting attacks in Internet of Things networks based on multi-task learning and hybrid sampling methods is proposed. An analysis and comparison of hard and soft parameter sharing in multi-task learning is carried out. The proposed approach aims to solve the problem of unbalanced traffic classification in IoT networks by random undersampling and synthetic sample generation using a pre-trained generative adversarial network model to achieve efficient data rebalancing.

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Section: релевантные работыunclassified

Section: (а) (б) рисунок 3 механизмы жесткого и мягкого совместного и...unclassified

Detecting Attacks on the Internet of Things Based on Multitasking Learning and Hybrid Sampling Methods

2024

Cybersecurity Issues

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“…Sensors, vehicles, tablets, wearable devices and more have joined the network, spawning a host of technologies and applications. For example, smart city, intelligent transportation [1], coastal monitoring [2], intelligent agriculture [3], augmented reality (AR)/virtual reality (VR), intelligent healthcare [4] and so on are developing rapidly. Global anytime and anywhere network access, large-scale machine-type communication and ultra-reliable low latency communication have become the development trend of the 6G network in the future [5].…”

Section: Introductionmentioning

confidence: 99%

Satellite-Air-Terrestrial Cloud Edge Collaborative Networks: Architecture, Multi-Node Task Processing and Computation

Liu,

Zhang,

Han

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Integrated satellite-terrestrial network (ISTN) has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere. Being a promising paradigm, cloud computing and mobile edge computing (MEC) have been identified as key technology enablers for ISTN to further improve quality of service and business continuity. However, most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks, ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites. In addition, most computing tasks are transferred to cloud servers or offloaded to nearby edge servers, the layered design of integrated satellite-air-terrestrial architecture and the cloudedge-device cooperative processing problems have not been fully considered. Therefore, different from previous works, this paper proposed a novel satelliteair-terrestrial layered architecture for cloud-edge-device collaboration, named SATCECN. Then this paper analyzes the appropriate deployment locations of cloud servers and edge servers in ISTN, and describes the processing flow of typical satellite computing tasks. For computing resource allocation problems, this paper proposed a device-edge-cloud Multi-node Cross-layer Collaboration Computing (MCCC) method to find the optimal task allocation strategy that minimizes the task completion delay and the weighted system energy consumption. Furthermore, the approximate optimal solutions of the optimization model are obtained by using successive convex approximation algorithm, and the outstanding advantages of the proposed method in reducing system energy consumption and task execution delay are verified through experiments. Finally, some potential issues and directions for future research are highlighted.

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Secure WSN Architecture Utilizing Hybrid Encryption with DKM to Ensure Consistent IoV Communication

Lilhore,

Simaiya,

Dalal

et al. 2024

Wireless Pers Commun

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Multitask Learning for Security and Privacy in IoV (Internet of Vehicles)

Cited by 20 publications

References 23 publications

Detecting Attacks on the Internet of Things Based on Multitasking Learning and Hybrid Sampling Methods

Detecting Attacks on the Internet of Things Based on Multitasking Learning and Hybrid Sampling Methods

Satellite-Air-Terrestrial Cloud Edge Collaborative Networks: Architecture, Multi-Node Task Processing and Computation

Secure WSN Architecture Utilizing Hybrid Encryption with DKM to Ensure Consistent IoV Communication

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