Cloud Storage untuk Embedded Intrusion Detection System

Abstract

The Corona Virus (COVID-19) pandemic has had a major social and economic impact on the world. Along with the potential challenges of sharing domains, brings with it many cybersecurity challenges that need to be addressed in a timely manner for critical infrastructure. The increase in the use of internet technology during this pandemic is directly proportional to the increase in the development of Information and Communication Technology (ICT) and cybercrime. Therefore, it is necessary to elaborate the existing ICTs to reduce the impact caused by attacks on the network according to the needs and capabilities of the users. This study applies a Network Intrusion Detection System (NIDS) based on the Raspberry Pi 4 Model B using Snort IDS with log storage media on cloud storage by visualizing the alerts generated to facilitate the analysis of anomalies that occur on the network. The result of this research is that there are attack signatures that are not available in the default rules so that further configuration is needed on Snort. The performance of the IDS sensor does not reduce the capability of the IDS sensor which acts as a hotspot when an attack occurs.