抄録
The application of many IoT devices is making our world more convenient and efficient. However, it also makes a large number of cyber-attacks possible because most IoT devices have very limited resources and cannot perform ordinary intrusion detection systems. How to implement efficient and lightweight IDS in IoT environments is a critically important and challenging task. Several detection systems have been implemented on Raspberry Pi, but most of them are signature-based and only allow limited rules. In this study, a lightweight IDS based on machine learning is implemented on a Raspberry Pi. To make the system lightweight, a correlation-based feature selection algorithm is applied to significantly reduce the number of features and a lightweight classifier is utilized. The performance of our system is examined in detail and the experimental result indicates that our system is lightweight and has a much higher detection speed with almost no sacrifice of detection accuracy.
| 元の言語 | 英語 |
|---|---|
| ホスト出版物のタイトル | Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019 |
| 編集者 | Makoto Takizawa, Tomoya Enokido, Leonard Barolli, Fatos Xhafa |
| 出版者 | Springer Verlag |
| ページ | 458-469 |
| ページ数 | 12 |
| ISBN（印刷物） | 9783030150310 |
| DOI | |
| 出版物ステータス | 出版済み - 1 1 2020 |
| イベント | 33rd International Conference on Advanced Information Networking and Applications, AINA-2019 - Matsue, 日本 継続期間: 3 27 2019 → 3 29 2019 |
出版物シリーズ
| 名前 | Advances in Intelligent Systems and Computing |
|---|---|
| 巻 | 926 |
| ISSN（印刷物） | 2194-5357 |
会議
| 会議 | 33rd International Conference on Advanced Information Networking and Applications, AINA-2019 |
|---|---|
| 国 | 日本 |
| 市 | Matsue |
| 期間 | 3/27/19 → 3/29/19 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science(all)
これを引用
Implementing Lightweight IoT-IDS on Raspberry Pi Using Correlation-Based Feature Selection and Its Performance Evaluation. / Soe, Yan Naung; Feng, Yaokai; Santosa, Paulus Insap; Hartanto, Rudy; Sakurai, Kouichi.
Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019. 版 / Makoto Takizawa; Tomoya Enokido; Leonard Barolli; Fatos Xhafa. Springer Verlag, 2020. p. 458-469 (Advances in Intelligent Systems and Computing; 巻 926).研究成果: 著書/レポートタイプへの貢献 › 会議での発言
}
TY - GEN
T1 - Implementing Lightweight IoT-IDS on Raspberry Pi Using Correlation-Based Feature Selection and Its Performance Evaluation
AU - Soe, Yan Naung
AU - Feng, Yaokai
AU - Santosa, Paulus Insap
AU - Hartanto, Rudy
AU - Sakurai, Kouichi
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The application of many IoT devices is making our world more convenient and efficient. However, it also makes a large number of cyber-attacks possible because most IoT devices have very limited resources and cannot perform ordinary intrusion detection systems. How to implement efficient and lightweight IDS in IoT environments is a critically important and challenging task. Several detection systems have been implemented on Raspberry Pi, but most of them are signature-based and only allow limited rules. In this study, a lightweight IDS based on machine learning is implemented on a Raspberry Pi. To make the system lightweight, a correlation-based feature selection algorithm is applied to significantly reduce the number of features and a lightweight classifier is utilized. The performance of our system is examined in detail and the experimental result indicates that our system is lightweight and has a much higher detection speed with almost no sacrifice of detection accuracy.
AB - The application of many IoT devices is making our world more convenient and efficient. However, it also makes a large number of cyber-attacks possible because most IoT devices have very limited resources and cannot perform ordinary intrusion detection systems. How to implement efficient and lightweight IDS in IoT environments is a critically important and challenging task. Several detection systems have been implemented on Raspberry Pi, but most of them are signature-based and only allow limited rules. In this study, a lightweight IDS based on machine learning is implemented on a Raspberry Pi. To make the system lightweight, a correlation-based feature selection algorithm is applied to significantly reduce the number of features and a lightweight classifier is utilized. The performance of our system is examined in detail and the experimental result indicates that our system is lightweight and has a much higher detection speed with almost no sacrifice of detection accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85064004822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064004822&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15032-7_39
DO - 10.1007/978-3-030-15032-7_39
M3 - Conference contribution
AN - SCOPUS:85064004822
SN - 9783030150310
T3 - Advances in Intelligent Systems and Computing
SP - 458
EP - 469
BT - Advanced Information Networking and Applications - Proceedings of the 33rd International Conference on Advanced Information Networking and Applications AINA-2019
A2 - Takizawa, Makoto
A2 - Enokido, Tomoya
A2 - Barolli, Leonard
A2 - Xhafa, Fatos
PB - Springer Verlag
ER -