Performance Evaluation of Single-Board Embedded Linux Platforms as Asterisk Servers for Phone of Things (PoT) Applications

Khalil, Haytham; Elgazzar, Khalid

doi:10.1109/iwcmc55113.2022.9824902

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…However, MQTT and CoAP are not applicable in advanced IoT applications that require high data rates and low latency [22,23]. Session Initiation Protocol (SIP) is considered as a good candidate, for supporting a wide variety of IoT application scenarios [7,11,22,23]. Besides instant messaging, it is suitable for handling short or long session semantics for streaming data and publish-subscribe semantics for event notifications, e.g., subscribe, publish, notify or method, thereby enabling more sophisticated IoT network operations.…”

Section: Introductionmentioning

confidence: 99%

SIP-DDoS: SIP Framework for DDoS Intrusion Detection Based on Recurrent Neural Networks

Sbai,

Allaert,

Sondi

et al. 2024

Machine Learning for Networking

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The rapid evolution of beyond fifth-generation (B5G) and sixth-generation (6G) networks has significantly driven the growth of Internet of Things (IoT) applications. These applications are characterised by: a massive connectivity, high security level, trust, wireless coverage, also ultra-low latency, high throughput, and ultra-reliability, especially for real-time oriented sessions or sensor like cameras. While traditional protocols like MQTT and CoAP are inadequate for such types of applications, under certain conditions, the 3GPP standard Session Initiation Protocol (SIP) emerges as a promising solution. However, SIP faces various Distributed Denial of Service (DDoS) threats, as INVITE flooding attacks presenting a significant challenge. This work presents a GRUbased Intrusion Detection System (IDS) to detect SIP-INVITE flooding attacks. Leveraging recurrent neural networks, the IDS efficiently process sequential SIP traffic data in real time, identifying attack patterns effectively. The GRU's ability to capture temporal dependencies enhances accuracy in classifying and detecting attack behaviors. The results demonstrate that the framework can effectively detect and mitigate INVITE flooding attacks of different intensities, under practical settings. The performance results show that the proposed framework is robust and can be practically deployed, e.g., inference time less than 800 µs and a marginal rate for the misclassified traffic.

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