Machine learning-based IoT-botnet attack detection with sequential architecture

Yan Naung Soe, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto, Kouichi Sakurai

研究成果: ジャーナルへの寄稿学術誌査読

81 被引用数 (Scopus)


With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.

ジャーナルSensors (Switzerland)
出版ステータス出版済み - 8月 2 2020

!!!All Science Journal Classification (ASJC) codes

  • 分析化学
  • 情報システム
  • 器械工学
  • 原子分子物理学および光学
  • 電子工学および電気工学
  • 生化学


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