The MPI Technology

Kurgalin, Sergei; Borzunov, Sergei

doi:10.1007/978-3-030-27558-7_4

Cited by 1 publication

(2 citation statements)

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“…This eliminates the data compilation process with the communication between the master and worker devices using open message passing interface (OpenMPI) through Python [16]- [18]. This interface spreads processes according to the allocated device processors available in the system before the commencement of the capture process [19]. According to preliminary studies [18], [19], the capture effectivity increases by running multiple processes in parallel limited to one wireless network interface.…”

Section: Network Topologymentioning

confidence: 99%

“…This interface spreads processes according to the allocated device processors available in the system before the commencement of the capture process [19]. According to preliminary studies [18], [19], the capture effectivity increases by running multiple processes in parallel limited to one wireless network interface. The topology requires properly implementing an algorithm and running inside the device.…”

Section: Network Topologymentioning

confidence: 99%

See 1 more Smart Citation

A new approach of scalable traffic capture model with Pi cluster

Hartomo

Daru

Purnomo

2023

IJECE

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<span lang="EN-US">The development of the internet of things (IoT), which functions as servers, device monitors, and controllers of several peripherals inside the smart home, eased workload in many sectors. Most devices are accessible through the internet because they communicate with wired or wireless interfaces. However, this feature makes them prone to the risk of being exposed to the public. The exposed devices are an easy target for the third party to launch a flooding attack through the network. This attack overloads the system due to the low processing capability, thereby interrupting any running process and harming the device. Therefore, this study proposed a scalable network capturing model that utilized multiple Raspberry Pi boards in parallel to monitor the network traffics simultaneously. An isolated experiment was used for evaluation by running simultaneous flooding attacks on each device. The result showed that the model consumed 30.44% more memory with 14.66% lower central processing unit (CPU) usage and 3.63% faster execution time. This means that this model is better in terms of performance and effectiveness than the single capture model.</span>

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