Insecurity of Operational IMS Call Systems: Vulnerabilities, Attacks, and Countermeasures

Lu, Yu-Han; Hsiao, Sandy Hsin-Yu; Li, Chi-Yu; Hsieh, Yi‐Chen; Chou, Po-Yi; Li, Yao-Yu; Xie, Tian; Tu, Guan-Hua

doi:10.1109/tnet.2022.3205183

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Cited by 3 publications

References 40 publications

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International Caller ID Spoofing Fraud Prevention for IP Multimedia Subsystem Networks

Wu,

Shen,

et al. 2023

2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE)

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International Caller ID Spoofing Fraud Prevention for IP Multimedia Subsystem Networks

Wu,

Shen,

et al. 2023

2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE)

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NReplay: 5G Key Reinstallation Attack Based on NAS Layer Vulnerabilities

Fan,

Shi,

Peng

2024

MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)

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Machine-learning-based optical spectrum feature analysis for DoS attack detection in IP over optical networks

Gong,

Lei,

Zhang

et al. 2024

Opt. Express

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In this paper, we introduce a novel approach for detecting Denial of Service (DoS) attacks in software-defined IP over optical networks, leveraging machine learning to analyze optical spectrum features. This method employs machine learning to automatically process optical spectrum data, which is indicative of network security status, thereby identifying potential DoS attacks. To validate its effectiveness, we conducted both numerical simulations and experimental trials to collect relevant optical spectrum datasets. We then assessed the performance of three machine learning algorithms XGBoost, LightGBM, and the BP neural network in detecting DoS attacks. Our findings show that all three algorithms demonstrate a detection accuracy exceeding 97%, with the BP neural network achieving the highest accuracy rates of 99.55% and 99.74% in simulations and experiments, respectively. This research not only offers a new avenue for DoS attack detection but also enhances early detection capabilities in the underlying optical network through optical spectrum data analysis.

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Insecurity of Operational IMS Call Systems: Vulnerabilities, Attacks, and Countermeasures

Cited by 3 publications

References 40 publications

International Caller ID Spoofing Fraud Prevention for IP Multimedia Subsystem Networks

International Caller ID Spoofing Fraud Prevention for IP Multimedia Subsystem Networks

NReplay: 5G Key Reinstallation Attack Based on NAS Layer Vulnerabilities

Machine-learning-based optical spectrum feature analysis for DoS attack detection in IP over optical networks

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