Malicious Flows Generator Based on Data Balanced Algorithm

I. Hsien Liu, Cheng En Hsieh, Wei Min Lin, Chu Fen Li, Jung Shian Li

研究成果: Conference contribution

摘要

As Internet technology gradually matures, the network structure becomes more complex. Therefore, the attack methods of malicious attackers are more diverse and change faster. Fortunately, due to the substantial increase in computer computing power, machine learning is valued and widely used in various fields. It has also been applied to intrusion detection systems. This study found that due to the imperfect data ratio of the unbalanced flow dataset, the model will be overfitting and the misjudgment rate will increase. In response to this problem, this research proposes to use the Cuckoo system to induce malicious samples to generate malicious traffic, to solve the data proportion defect of the unbalanced traffic dataset.

原文English
主出版物標題2021 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665433587
DOIs
出版狀態Published - 2021
事件2021 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2021 - Taitung, Taiwan
持續時間: 2021 10月 52021 10月 8

出版系列

名字2021 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2021

Conference

Conference2021 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2021
國家/地區Taiwan
城市Taitung
期間21-10-0521-10-08

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦科學應用
  • 決策科學(雜項)
  • 統計、概率和不確定性
  • 邏輯

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