Abstract
<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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