15:20 〜 15:40
[3N3-IS-2e-01] Anomaly Detection of IoT Cyber Attacks in Smart City Using Machine Learning
キーワード：IoT
In this research, we aim to propose a new system of Networked Intrusion Detection System (NIDS) using machine learning, which aims to prevent known or unknown attacks by monitoring network traffic, taking into account the resource limitations of IoT devices.
The random forest used in previous studies cannot learn to take into account the time series of network traffic. Therefore, in this study, we propose a time-series-aware machine learning method using CNNs, which can learn from multiple network traffic as input.
In our experiments, we tried to detect anomalies in traffic by using 10 network traffic as input to the CNN.
As a result, we were able to show that our method can detect anomalies in network traffic with higher accuracy than the random forest method used in previous studies.
The random forest used in previous studies cannot learn to take into account the time series of network traffic. Therefore, in this study, we propose a time-series-aware machine learning method using CNNs, which can learn from multiple network traffic as input.
In our experiments, we tried to detect anomalies in traffic by using 10 network traffic as input to the CNN.
As a result, we were able to show that our method can detect anomalies in network traffic with higher accuracy than the random forest method used in previous studies.
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