발간년도 : [2024]
| 논문정보 |
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| 논문명(한글) |
[Vol.19, No.1] A Detection Algorithm Based on Fuzzy Cognitive Maps for IoT |
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| 논문투고자 |
Se Yul Lee |
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| 논문내용 |
We are living in an accelerated environment of great digital transformation. The reason is because of the various smart devices and IoT devices in our daily lives. Security issues of IoT devices are increasingly causing damage to our daily lives. Some hacking incidents are as a follows: Personal life of ordinary households leaked to the Dark-web through wall pad hacking in Nov. 2021, Thermal imaging camera face and voice information collection and external transmission in Mar. 2021, Door opening through door lock wireless signal hacking in Sep. 2020. There were 10,665 privacy infringement incidents after hacking of over 1,800 IP cameras in Oct. 2019. In this paper, we propose a detection algorithm using fuzzy recognition in edge computing in IoT environment. And the objective performance evaluation data sets use NSL-KDD and CICDDOS2019. The detection algorithm applies SPuF-FCM(Fuzzy Cognitive Maps), FCM-OW(FCM with oriented weight), FCM-Self(FCM with Self-adaptive module). The performance evaluation result of the NSL-KDD and CICDDOS2019 data sets show that the hybrid intrusion detection algorithm using FCM-Self has better performance than the FCM & FCM-OW detection methods in edge computing IoT environment. And CICDDOS2019 improves the detection rate in DoS & DDoS attack such as Smurf than NSL-KDD data sets. |
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