A Secure Integrated Framework for Fog-Assisted Internet of Things Systems

Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices

Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset

Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares Naïve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset

An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions

The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

A smart resource management mechanism with trust access control for cloud computing environment

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead

FASTCloud: A framework of assessment and selection for trustworthy cloud service based on QoS

By virtue of technology and benefit advantages, cloud computing has increasingly attracted a large number of potential cloud consumers (PCC) plan to migrate the traditional business to the cloud service. However, trust has become one of the most challenging issues that prevent the PCC from adopting cloud services, especially in trustworthy cloud service selection. Besides, due to the diversity and dynamic of quality of service (QoS) in the cloud environment, the existing trust assessment methods based on the single constant value of QoS attribute and the subjective weight assignment are not good enough to provide an effective solution for PCCs to identify and select a trustworthy cloud service among a wide range of functionally-equivalent cloud service providers (CSPs). To address the challenge, a novel assessment and selection framework for trustworthy cloud service, FASTCloud, is proposed in this study. This framework facilitates PCCs to select a trustworthy cloud service based on their actual QoS requirements. In order to accurately and efficiently assess the trust level of cloud services, a QoS-based trust assessment model is proposed. This model represents a trust level assessment method based on the interval multiple attributes with an objective weight assignment method based on the deviation maximization to adaptively determine the trust level of different cloud services provisioned by candidate CSPs. The advantage of the proposed trust level assessment method in time complexity is demonstrated by the performance analysis and comparison. The experimental result of a case study with an open-source dataset shows that the trust model is efficient in cloud service trust assessment and the FASTCloud can effectively help PCCs select a trustworthy cloud service