A hybrid deep learning model for detecting DDoS flooding attacks in SIP-based systems

Younes, Osama S.

doi:10.1016/j.comnet.2023.110146

Computer Networks

2024

DOI: 10.1016/j.comnet.2023.110146

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A hybrid deep learning model for detecting DDoS flooding attacks in SIP-based systems

Osama S. Younes

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Cited by 3 publications

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DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment

Srinivasa Rao,

Santosh Kumar Patra,

Narayana

et al. 2024

Egyptian Informatics Journal

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DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment

Srinivasa Rao,

Santosh Kumar Patra,

Narayana

et al. 2024

Egyptian Informatics Journal

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Smart network security using advanced ensemble-DDoS attack detection and hybrid JA-SLOA-linked optimal routing-based mitigation

Dora,

Lakshmi

2024

Australian Journal of Electrical and Electronics Engineering

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Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams

Nedyalkov

2024

Telecom

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In today’s digital world, no one and nothing is safe from potential cyberattacks. There is also no 100% protection from such attacks. Therefore, it is advisable to carry out various studies related to the effects of the different cyberattacks on the performance of the specific devices under attack. In this work, a study was carried out to determine how individual TCP DoS attacks affect the parameters of VoIP (Voice over IP) voice and video streams. For the purpose of this work, a model of a simple IP network has been created using the GNS3 IP network-modeling platform. The VoIP platform used was Asterisk Free PBX. Tools from Kali Linux were used to implement the individual TCP DoS attacks; IP-network-monitoring tools and round-trip-delay-measurement tools were also used. The proposed study is applicable to multiple VoIP platforms wherein voice and video traffic are passed/processed by the VoIP server. From the obtained results, it was found that Asterisk Free PBX is very well secured against TCP DoS attacks, which do not affect the platform performance or the parameters of the voice and video streams. The values of the observed parameters, such as jitter, packet loss, round-trip delay, etc., are very far from the maximum allowable values. We also observed a low load on the CPU and RAM of the system during the whole study.

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A hybrid deep learning model for detecting DDoS flooding attacks in SIP-based systems

Cited by 3 publications

References 24 publications

DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment

DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment

Smart network security using advanced ensemble-DDoS attack detection and hybrid JA-SLOA-linked optimal routing-based mitigation

Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams

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