| Title | Malicious Flows Generator Based on Data Balanced Algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, I-Hsien, Hsieh, Cheng-En, Lin, Wei-Min, Li, Chu-Fen, Li, Jung-Shian |
| Conference Name | 2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY) |
| Keywords | Anomaly Traffic Detection, Computational modeling, Computer hacking, feature extraction, Generative Adversarial Learning, generative adversarial networks, Generators, IDS datasets, Intrusion detection, machine learning, malware analysis, Metrics, pubcrawl, resilience, Resiliency, sandbox, Scalability |
| Abstract | 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. |
| DOI | 10.1109/iFUZZY53132.2021.9605084 |
| Citation Key | liu_malicious_2021 |
Malicious Flows Generator Based on Data Balanced Algorithm