SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices

Khan, Md Sajid; Farzaneh, Behnam; Shahriar, Nashid; Saha, Niloy; Boutaba, Raouf

doi:10.1109/fnwf55208.2022.00117

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…The impact of the DDoS attack in 5G network slicing was also studied by Khan et al [3]. The authors deploy a 5G testbed using Free5GC [9] and UERANSIM [11].…”

Section: B Impact Of Attacks On 5g Slicingmentioning

confidence: 99%

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On the Impact of Flooding Attacks on 5G Slicing with Different VNF Sharing Configurations

AbdulGhaffar,

Mahyoub,

Matrawy

2024

2024 20th International Conference on the Design of Reliable Communication Networks (DRCN)

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Virtualized Network Function (VNF) sharing among multiple Fifth Generation (5G) slices allows network operators to increase the efficiency and utilization of the network. However, this sharing of VNFs can result in a degradation of the performance of the slices in the presence of an attack. In this paper, we evaluate the impact of flooding attacks on the performance of 5G slices with different VNF sharing configurations. We consider two VNF sharing configurations, in the first configuration, the Session Management Function (SMF) and User Plane Function (UPF) are shared among the deployed slices, while the SMF and UPF of the slices are isolated in the second configuration. The performance of these configurations is evaluated using different traffic types under two flooding attack scenarios; a ping flood attack targeting the data plane of 5G network, and a registration request flood attack directed at the control plane of 5G network. Our results showed different responses in the control and data planes. In the data plane, isolating VNFs of the slices provides better performance and mitigates the adverse effects of the attacks studied.

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“…The impact of the DDoS attack in 5G network slicing was also studied by Khan et al [3]. The authors deploy a 5G testbed using Free5GC [9] and UERANSIM [11].…”

Section: B Impact Of Attacks On 5g Slicingmentioning

confidence: 99%

“…On the other hand, sharing VNFs between deployed slices may expose users of one slice to the threats and attacks originating from other slices [1], [2]. For example, studies [3] have shown that Distributed Denial-of-Service (DDoS) attacks on one slice can affect the services of other slices if they share the underlying physical resources.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Flooding Attacks on 5G Slicing with Different VNF Sharing Configurations

AbdulGhaffar,

Mahyoub,

Matrawy

2024

2024 20th International Conference on the Design of Reliable Communication Networks (DRCN)

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“…One of the continuously mentioned threats is the false base station attack. Malicious actors deploy false base stations by impersonating legitimate base stations, allowing them to steal important resources such as user's personal information or exhaust resources through DoS attacks (Khan et al, 2022). The primary objective of threats originating from false base stations is often to compromise user availability.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Implementation of a Network Function for Real-time False Base Station Detection for the Next Generation Mobile Communication Environment

Son,

Park,

Kim

et al. 2024

JOWUA

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The threat posed by false base stations remains pertinent across the 4G, 5G, and forthcoming 6G generations of mobile communication. In response, this paper introduces a real-time detection method for false base stations employing two approaches: machine learning and specification-based. Utilizing the UERANSIM open 5G RAN (Radio-Access Network) test platform, we assess the feasibility and practicality of applying these techniques within a 5G network environment. Emulating real-world 5G conditions, we implement a functional split in the 5G base station and deploy the False Base Station Detection Function (FDF) as a 5G NF (Network Function) within the CU (Central Unit). This setup enables real-time detection seamlessly integrated into the network. Experimental results indicate that specification-based detection outperforms machine learning, achieving a detection accuracy of 99.6% compared to 96.75% for the highest-performing machine learning model XGBoost. Furthermore, specification-based detection demonstrates superior efficiency, with CPU usage of 1.2% and memory usage of 16.12MB, compared to 1.6% CPU usage and 182.4MB memory usage for machine learning on average. Consequently, the implementation of specification-based detection using the proposed method emerges as the most effective technique in the 5G environment.

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“…Another security challenge with SDN/NFVbased 5G networks is that attackers are increasingly targeting them and are vulnerable to various attacks, including distributed denial-of-service (DDoS) attacks, man-in-the-middle Electronics 2024, 13, 1515 2 of 23 attacks, and malware attacks [1]. These attacks can disrupt or disable SDN/NFV-based 5G networks and compromise mobile users' data [2].…”

Section: Introductionmentioning

confidence: 99%

“…Detecting and preventing intrusions in SDN/NFV-based networks is a motivating issue that has attracted significant research attention. Researchers are actively developing new intrusion detection and mitigation systems [2][3][4] designed explicitly for SDN/NFV-based 5G networks, and these systems are expected to play a vital role in securing the next generation of cellular networks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layered Defence Strategy against DDoS Attacks in SDN/NFV-Based 5G Mobile Networks

Sheibani,

Konur,

Awan

et al. 2024

Electronics

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Software-defined networking (SDN) and network functions virtualisation (NFV) are crucial technologies for integration in the fifth generation of cellular networks (5G). However, they also pose new security challenges, and a timely research subject is working on intrusion detection systems (IDSs) for 5G networks. Current IDSs suffer from several limitations, resulting in a waste of resources and some security threats. This work proposes a new three-layered solution that includes forwarding and data transport, management and control, and virtualisation layers, emphasising distributed controllers in the management and control layer. The proposed solution uses entropy detection to classify arriving packets as normal or suspicious and then forwards the suspicious packets to a centralised controller for further processing using a self-organising map (SOM). A dynamic OpenFlow switch relocation method is introduced based on deep reinforcement learning to address the unbalanced burden among controllers and the static allocation of OpenFlow switches. The proposed system is analysed using the Markov decision process, and a Double Deep Q-Network (DDQN) is used to train the system. The experimental results demonstrate the effectiveness of the proposed approach in mitigating DDoS attacks, efficiently balancing controller workloads, and reducing the duration of the balancing process in 5G networks.

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SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices

Cited by 14 publications

References 14 publications

On the Impact of Flooding Attacks on 5G Slicing with Different VNF Sharing Configurations

On the Impact of Flooding Attacks on 5G Slicing with Different VNF Sharing Configurations

A Study on the Implementation of a Network Function for Real-time False Base Station Detection for the Next Generation Mobile Communication Environment

A Multi-Layered Defence Strategy against DDoS Attacks in SDN/NFV-Based 5G Mobile Networks

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