Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond

Thantharate, Anurag; Paropkari, Rahul Arun; Walunj, Vijay; Beard, Cory; Kankariya, Poonam

doi:10.1109/ccwc47524.2020.9031158

Cited by 80 publications

(37 citation statements)

References 11 publications

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“…This way, suspicious traffic can be further analyzed before blocking its sessions without affecting legitimate traffic. In [ 25 ] a quarantine slice is used to isolate suspicious flows. However, this work focuses on the detection of malicious nodes (mainly by analyzing network slice requests) at a high level and it does not propose any architecture for diverting malicious flows to a quarantine slice.…”

Section: Related Workmentioning

confidence: 99%

Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks

Candal‐Ventureira

Fondo‐Ferreiro

Gil‐Castiñeira

et al. 2020

Sensors

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The unstoppable adoption of the Internet of Things (IoT) is driven by the deployment of new services that require continuous capture of information from huge populations of sensors, or actuating over a myriad of “smart” objects. Accordingly, next generation networks are being designed to support such massive numbers of devices and connections. For example, the 3rd Generation Partnership Project (3GPP) is designing the different 5G releases specifically with IoT in mind. Nevertheless, from a security perspective this scenario is a potential nightmare: the attack surface becomes wider and many IoT nodes do not have enough resources to support advanced security protocols. In fact, security is rarely a priority in their design. Thus, including network-level mechanisms for preventing attacks from malware-infected IoT devices is mandatory to avert further damage. In this paper, we propose a novel Software-Defined Networking (SDN)-based architecture to identify suspicious nodes in 4G or 5G networks and redirect their traffic to a secondary network slice where traffic is analyzed in depth before allowing it reaching its destination. The architecture can be easily integrated in any existing deployment due to its interoperability. By following this approach, we can detect potential threats at an early stage and limit the damage by Distributed Denial of Service (DDoS) attacks originated in IoT devices.

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Section: Related Workmentioning

confidence: 99%

Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks

Candal‐Ventureira

Fondo‐Ferreiro

Gil‐Castiñeira

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Rapid growth in devices and user demands regarding mobile communication results high speed 3G, 4G, and upcoming 5th generation networks. Due to the richer mobility, reliability, and services associated with 5G network, the operators are constantly working in cooperating these feature for attracting more users and to provide better quality of services [3,24]. Among the prominent feature of 5G technology, network slicing is one of the key element which will allow the operator to customize their capabilities and services according to the for the selection of network slice, their standardization, and different slice-independents functions [11].…”

Section: The Role Of Machine Learning In 5g Network Slicingmentioning

confidence: 99%

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Khan

Ali

et al. 2022

J Netw Syst Manage

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In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

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“…Moreover, the novel issues related to privacy have also been raised in the era of COVID-19 due to transmitting large amount of personal information over wireless networks Fang and Qian (2020). Various proposals and frameworks have recently been proposed to ensure reliable delivery of medical grade data using the uRLLC and mMTC scenarios, such as the use of security capacity formula Ren et al (2020) and deep learning models Thantharate et al (2020).…”

Section: Expected Challenges and Future Research Directions Challengesmentioning

confidence: 99%

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

et al. 2021

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Social distancing has been regarded as a key method by the authorities worldwide to manage the pandemic of COVID-19. Digital technologies play a crucial role to support the social, professional and economic activities when people are forced to stay locked-down in their homes. Internet of things (IoT) technologies have a track of providing high quality remote health care and automation services which could guarantee social distancing while maintaining health and well-being of populations. In this paper, we propose an end-to-end IoT architecture to support the social distancing in the event of pandemic. The architecture comprises of the major use cases of IoT in relevance with the COVID-19. Furthermore, we also present a short-term and long-term strategy to mange the social distancing methodology using the proposed IoT architecture. The challenges associated with each layer of architecture have been highlighted and design guidelines have been presented to deal with them.

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Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond

Cited by 80 publications

References 11 publications

Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks

Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

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