Enhancing Security of Software Defined Mobile Networks

Liyanage, Madhusanka; Ahmed, I.; Okwuibe, Jude; Ylianttila, Mika; Kabir, Hammad; Santos, Jesús Llorente; Kantola, Raimo; Perez, Oscar López; Itzazelaia, Mikel Uriarte; de, Edgardo Montes

doi:10.1109/access.2017.2701416

Cited by 38 publications

(15 citation statements)

References 26 publications

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“…The telecommunications vertical is one of the sectors most exposed to cyberattacks. Moreover, attacks are targeted on its many dimensions like infrastructure [147], communication [148], isolation [149] and many more. Another thing that is worth mentioning is presented in papers [150], [151], and concerns attacks that are not focused on the operator but affect its network, for example, high traffic caused by Denial of Service impacts its operation of all other network elements.…”

Section: ) Attacks and Countermeasuresmentioning

confidence: 99%

Verticals in 5G MEC-Use Cases and Security Challenges

et al. 2021

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5G is the fifth-generation cellular network satisfying the requirements IMT-2020 (International Mobile Telecommunications-2020) of the International Telecommunication Union. Mobile network operators started using it worldwide in 2019. Generally, 5G achieves exceptionally high values of performance parameters of access and transmission. The application of edge servers facilitated the implementation of such requirements of 5G, which resulted in 5G MEC (Multi-access Edge Computing) technology. Moreover, to optimize services for specific business applications, the concept of 5G vertical industries has been proposed. In this paper, we study how the application of the MEC technology affects the functioning of 5G MEC-based services. We consider twelve representative vertical industries of 5G MEC by presenting their essential characteristics, threats, vulnerabilities, and known attacks. Next, we analyze their functional properties, give efficiency patterns and identify the effect of applying the MEC technology in 5G on the resultant network's quality parameters to identify the expected security requirements. Finally, we identify the impact of classified threats on the 5G empowered vertical industries and identify the most sensitive cases to focus on their protection against network attacks in the first place.

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Section: ) Attacks and Countermeasuresmentioning

confidence: 99%

Verticals in 5G MEC-Use Cases and Security Challenges

et al. 2021

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“…Network virtualization separates the network software logic from the hardware logic to enable sharing the network resources among multiple users (tenants) [249], [250]. By decoupling the network functions from hardware, NFV enables mapping multiple network functions (software instances) to [249], [251], [252], and beyond the layers. In the following sub-sections, we discuss how ML-based approaches can improve the efficiency of SDN and NFV.…”

Section: ML For Sdn and Nfvmentioning

confidence: 99%

“…By introducing programmability and centralizing the network control, SDN along with all benefits also opens networks to security challenges, as detailed in [259]. The centralized control frameworks, such as SDN controllers, oversee and control the entire network from a central vintage point, making it a favorite choice for resource exhaustion and Denial of Service (DoS) attacks [252]. Overwhelming the controller with any kind of traffic can cause a jamming effect, resource exhaustion, or a DoS attack as described in [261], [262].…”

Section: A ML For Sdnmentioning

confidence: 99%

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

et al. 2020

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The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI) I. INTRODUCTION T HE security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. The requirement for human interaction or manual configuration constitutes a major hindrance for a network to use its past experiences to adapt to changing requirements. The general idea is to predict the (future) behavior of a service, network segment, user or User Equipment (UE), and tune the network at run-time based on this information. For instance, the movement trajectory of a user can be predicted using

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“…Liyanage et al, [31] proposed SIGMONA security architecture, a multi-tier security approach with four component: Secure Communication Component, Policy-Based Communication (PBC) Component, Security Management and Monitoring Component and Synchronized Network Security and Traffic Component. Within the PBC Component, there are included TCP-Splicing mechanisms with a bot detection scheme, for mitigating DDoS attacks targeted against the control plane.…”

Section: State Of the Artmentioning

confidence: 99%

Towards the transversal detection of DDoS network attacks in 5G multi-tenant overlay networks

Serrano-Mamolar

Pervez

Calero

et al. 2018

Computers & Security

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Currently, there is no any effective security solution which can detect cyber-attacks against 5G networks where multitenancy and user mobility are some unique characteristics that impose significant challenges over such security solutions. This paper focuses on addressing a transversal detection system to be able to protect at the same time, infrastructures, tenants and 5G users in both edge and core network segments of the 5G multitenant infrastructures. A novel approach which significantly extends the capabilities of a commonly used IDS, to accurately identify attacking nodes in a 5G network, regardless of multiple network traffic encapsulations, has been proposed in this paper. The proposed approach is suitable to be deployed in almost all 5G network segments including the Mobile Edge Computing. Both architectural design and data models are described in this contribution. Empirical experiments have been carried out a realistic 5G multi-tenant infrastructures to intensively validate the design of the proposed approach regarding scalability and flexibility.

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Enhancing Security of Software Defined Mobile Networks

Cited by 38 publications

References 26 publications

Verticals in 5G MEC-Use Cases and Security Challenges

Verticals in 5G MEC-Use Cases and Security Challenges

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Towards the transversal detection of DDoS network attacks in 5G multi-tenant overlay networks

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