Assuring virtual network function image integrity and host sealing in Telco cloue

Lal, Shankar; Ravidas, Sowmya; Oliver, Ian; Taleb, Tarik

doi:10.1109/icc.2017.7997299

Cited by 17 publications

(12 citation statements)

References 14 publications

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“…4c), allowing the configuration to be suited for the Smart Manufacturing threshold when γ = 0.0001 and even the Transport Industry threshold when γ = 0.00001, besides presenting a very similar behavior compared to the configuration with 8 VMs, 2 containers (γ = 0.001, red line with crosses). These findings evince what level of improvement that containerized VNFs must achieve to meet specific requirements, recalling that both software and hardware share relevance towards this aspect, as investigated in [25].…”

Section: Improved Failure Ratesmentioning

confidence: 73%

Availability and Reliability in MEC-enabled UAVs for URLLC

Falcao

Balieiro

et al. 2021

Preprint

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Section: Improved Failure Ratesmentioning

confidence: 73%

Availability and Reliability in MEC-enabled UAVs for URLLC

Falcao

Balieiro

et al. 2021

Preprint

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“…Fine-grained access control needs to be investigated with appropriate encryption to ensure a secure collaboration and interoperability between heterogeneous resources and different operational parties. A state-of-the-art analysis on security and privacy issues associated with edge cloud is performed in [206], while a preliminary study on MEC security is presented in [207]- [209]. Various intrusion detection techniques are in place for cloud computing, but for large-scale geo-distributed environments, this is still a challenge.…”

Section: Mec Security and Privacymentioning

confidence: 99%

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

Taleb

Samdanis

Mada

et al. 2017

IEEE Commun. Surv. Tutorials

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746

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Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multitenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

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“…The ETSI NFV architecture identifies three main building blocks: Introducing virtualized network resources into the IoT ecosystem brings multiple value-added features, accounting for their heterogeneity and rapid growth. When coupled with SDN, NFV can not only, provide advanced virtual monitoring tools, such as Intrusion Detection Systems (IDSs) and Deep Packet Inspectors (DPIs), but also provision, and configure on-demand and scalable network security appliances, such as firewalls and authentication systems, in order to cope with the attacks detected by the monitoring agents [24], [25]. Moreover, offloading the extra processing induced by security from these resource-constrained IoT devices to virtual instances [26] saves energy and improves efficiency leaving more headroom to other useful applications.…”

Section: ) Network Function Virtualizationmentioning

confidence: 99%

A Machine Learning Security Framework for Iot Systems

et al. 2020

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Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AIbased reaction agent that use ML-Models divided into network patterns analysis, along with anomalybased intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomalybased intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one-class SVM. The detection accuracy of anomalies achieved 99.71%. A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.

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Assuring virtual network function image integrity and host sealing in Telco cloue

Cited by 17 publications

References 14 publications

Availability and Reliability in MEC-enabled UAVs for URLLC

Availability and Reliability in MEC-enabled UAVs for URLLC

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

A Machine Learning Security Framework for Iot Systems

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