Machine Learning Based Signaling DDoS Detection System for 5G Stand Alone Core Network

Park, Seongmin; Cho, Byungsun; Kim, Dowon; You, Ilsun

doi:10.3390/app122312456

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Starting with the first study conducted by study [8], this research addresses the pressing issue of distributed denial-ofservice (DDoS) attacks within the context of 5G networks. It emphasizes the predominant focus of previous studies on radio access networks (RAN) and voice service networks, often overlooking the vulnerabilities inherent in core networks (CN).…”

Section: Related Workmentioning

confidence: 99%

DDoS Classification using Combined Techniques

Yusof,

Safar,

Abdullah

et al. 2024

IJACSA

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Now-a-days, the attacker's favourite is to disrupt a network system. An attacker has the capability to generate various types of DDoS attacks simultaneously, including the Smurf attack, ICMP flood, UDP flood, and TCP SYN flood. This DDoS issue encouraged the design of a classification technique against DDoS attacks that enter a computer network environment. The technique is called Packet Threshold Algorithm (PTA) and is combined with several machine learning to classify incoming packets that have been captured and recorded. Apart from that, the combination of techniques can differentiate between normal packets and DDoS attacks. The performance of all techniques in the research achieved high detection accuracy while mitigating the issue of a high false positive rate. The four techniques focused in this research are PTA-SVM, PTA-NB, PTA-LR and PTA-KNN. Based on the results of detection accuracy and false positive rate for all the techniques involved, it proves the PTA-KNN technique is a more effective technique in the context of detection of incoming packets whether DDoS attacks or normal packets.

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Section: Related Workmentioning

confidence: 99%

DDoS Classification using Combined Techniques

Yusof,

Safar,

Abdullah

et al. 2024

IJACSA

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“…Sattar and Matrawy [52] used a slice isolation approach to detect and mitigate such attacks. Additionally, various other methods have been introduced, including those utilizing DL [53], Reinforcement Learning [54], and ML [55] techniques. These approaches can be used for flexible protection systems against DDoS threats in current scenarios.…”

Section: ) Ddos Attackmentioning

confidence: 99%

Security in 5G Network Slices: Concerns and Opportunities

Singh,

Hegde

et al. 2024

IEEE Access

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Network slicing has emerged as a cornerstone technology within the 5G ecosystem, enabling efficient resource allocation, service customization, and support for various applications. Its ability to deliver Network-as-a-Service (NaaS) brings a new paradigm of adaptable and efficient network provisioning. However, with the diversification of services and the increasing complexity of network infrastructures, a simultaneous rise in security vulnerabilities becomes evident. These flaws go beyond the limitations of conventional network security and affect various aspects of network slice (NS) implementation and management. The limitations of traditional security, such as static policies, single point of failure, and challenges in effectively securing network slicing deployments, underscore the need to explore security measures tailored to the dynamic nature of 5G networks. To ensure the robust security of 5G networks, it is essential to consider various security concerns such as isolation, authentication, and authorization. Furthermore, dynamic orchestration and inter-slice communication security challenges must be proactively tackled. The security concerns related to 5G networks must be addressed comprehensively to ensure the safe and secure operation of the network. Our survey paper goes into these complex security issues, providing an in-depth and systematic review of the various contexts in which they emerge. We have identified the five most vulnerable areas in Network Slicing: Slice-Lifecycle, Communication type slice uses, Technologies used to provide service, Management threats, and End Devices utilized in service. Apart from threats in these vulnerable areas, we also discussed a few generous attacks that can be launched to disrupt network-slicing services. Furthermore, this study is a valuable resource for evaluating the current state of research efforts in this domain, contributing to the ongoing enhancement of security measures and the overall robustness of network-slicing technology. In doing so, we aim to ensure the secure and sustainable evolution of 5G networks as they become increasingly integral to our digital infrastructure.

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“…The rapid development of 5g technologies also leads to the rapid spread of DDoS attacks in 5g network environments. The work "Composite and efficient DDoS attack detection framework for B5G networks" [70] describes the narrowing of machine learning for the detection of DDoS attacks taking place in 5G networks. The work describes the use of the DNN network in the detection of these attacks in four scenarios, where the measurements achieved accuracy ratings ranging from 71% to 99%.…”

Section: Further Researchmentioning

confidence: 99%

Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

et al. 2023

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This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives.

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Machine Learning Based Signaling DDoS Detection System for 5G Stand Alone Core Network

Cited by 7 publications

References 13 publications

DDoS Classification using Combined Techniques

DDoS Classification using Combined Techniques

Security in 5G Network Slices: Concerns and Opportunities

Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

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