DeepSecure: Detection of Distributed Denial of Service Attacks on 5G Network Slicing—Deep Learning Approach

Kuadey, Noble; Maale, Gerald Tietaa; Kwantwi, Thomas; Liu, Guisong

doi:10.1109/lwc.2021.3133479

Cited by 42 publications

(27 citation statements)

References 22 publications

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“…Thus, collaborative learning for detecting inter-slicing 5G V2X attacks is not yet explored. Moreover, The authors of [11] and [12] proposed a DL-based attack detection scheme for distributed denial of service (DDoS) attacks in 5G networks. However, these schemes are centralized and are only proposed to detect DDoS attacks on the core network and consider neither the 5G New Radio (NR) attacks, including V2X and the MEC, nor V2X-NS attacks.…”

Section: Related Workmentioning

confidence: 99%

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Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

Boualouache

Engel

2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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As a leading enabler of 5G, Network Slicing (NS) aims at creating multiple virtual networks on the same shared and programmable physical infrastructure. Integrated with 5G-Vehicle-to-Everything (V2X) technology, NS enables various isolated 5G-V2X networks with different requirements such as autonomous driving and platooning. This combination has generated new attack surfaces against Connected and Automated Vehicles (CAVs), leading them to road hazards and putting users' lives in danger. More specifically, such attacks can either intraslice targeting the internal service within each V2X Network Slice (V2X-NS) or inter-slice targeting the cross V2X-NSs and breaking the isolation between them. However, detecting such attacks is challenging, especially inter-slice V2X attacks where security mechanisms should maintain privacy preservation and NS isolation. To this end, this paper addresses detecting interslice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and Deep learning (DL) together with Federated learning (FL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) over V2X-NSs. Our privacy preservation scheme is hierarchical and supports FLbased collaborative learning. It also integrates a game-theorybased mechanism to motivate FL clients (CAVs) to provide highquality DL local models. We train, validate, and test our scheme using a publicly available dataset. The results show our scheme's accuracy and efficiency in detecting inter-slice V2X attacks.

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Section: Related Workmentioning

confidence: 99%

“…Unlike the schemes above, we exploit FL, DL, and VSaS to propose collaborative learning enabling scheme for detecting inter-slicing 5G V2X attacks. VSaS is a flexible and elastic approach, supporting the 5G security concept, which consists in integrating sVNFs in the slice life cycle [12].…”

Section: Related Workmentioning

confidence: 99%

Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

Boualouache

Engel

2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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“…With more layers and neurons, the deep neural network (DNN) has a greater ability to generalize and learn when large datasets are used [6]- [8] and more complex feature mappings [9] than the typical three-layer neural network. There is currently a DNN model for the automatic classification of modulation in communication techniques [9], and the DNN structure of encoders have been implemented to minimize the difficulties of peak-average power ratio and symbol identification against Doppler frequency shift [10], [11].…”

Section: A Related Workmentioning

confidence: 99%

Deep Learning Gated Recurrent Neural Network-Based Channel State Estimator for OFDM Wireless Communication Systems

et al. 2022

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Using deep learning technologies, the channel estimate for an orthogonal frequency division multiplexing system (OFDM) based on pilots is done in this work. To be more specific, deep learning gated recurrent unit (GRU) neural networks are used to present a new framework for channel estimation. Initially, it is trained offline using generated data sets, and thereafter it is used online to track the channel parameters, after which the data transmitted can be recovered. For the purpose of determining the performance of the proposed estimator, three alternative deep learning optimization techniques are used to test it. It is also compared to other commonly used estimators, such as least squares (LS) and minimum mean square error (MMSE). In addition, the proposed estimator is compared with two existing models. Deep learning GRU neural network-based channel state estimator, which are capable of learning and generalizing rapidly, are shown to outperform the comparable estimators when just a few pilots are available. In addition, there is no need for prior knowledge of channel statistics. So, estimating OFDM communication system channel states using the proposed estimator appears promising.INDEX TERMS Deep learning, channel estimation, gated recurrent unit, OFDM.

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“…Although different attack detection systems with different architectures (centralized, distributed) and different learning methods (supervised, unsupervised, and hybrid) have been proposed, intra-slice V2X attack detection is not addressed yet. Thantharate et al [12] and Kuadey et al [13] proposed a DLbased attack detection system for Distributed Denial of Service (DDoS) attacks for 5G networks. However, these systems are (i) only designed to detect DDoS against the core network and (ii) do not consider attacks on the network access part, including V2X and the MEC.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks

Boualouache

Djaidja

Senouci

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims to create Vehicle-to-Everything (V2X) network slices with different network requirements on a shared and programmable physical infrastructure. However, NS has generated new network threats that might target CAVs leading to road hazards. More specifically, such attacks may target either the inner functioning of each V2X-NS (intra-slice) or break the NS isolation. In this paper, we aim to deal with the raised question of how to detect intra-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and deep learning (DL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) within V2X-NSs. These sVNFs are in charge of detecting such attacks, thanks to a DL model that we also build in this work. The proposed DL model is trained, validated, and tested using a publicly available dataset. The results show the efficiency and accuracy of our scheme to detect intra-slice V2X attacks.

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DeepSecure: Detection of Distributed Denial of Service Attacks on 5G Network Slicing—Deep Learning Approach

Cited by 42 publications

References 22 publications

Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

Federated Learning-based Inter-slice Attack Detection for 5G-V2X Sliced Networks

Deep Learning Gated Recurrent Neural Network-Based Channel State Estimator for OFDM Wireless Communication Systems

Deep Learning-based Intra-slice Attack Detection for 5G-V2X Sliced Networks

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