| Presentation | 2022-07-22 A study on deep learning-based cyber attack detection Ruei-Fong Hong, Shih-Cheng Horng, Qiangfu Zhao, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Cyberattack is a broad term for cybercrime that includes any deliberate attack on a computer device, network or infrastructure that renders software, systems or services inoperable. Such attacks can be carried out by individuals (e.g., hackers) or organizations, and can target individuals, organizations, or even countries. In this study, we try to select the best machine learning model in terms of accuracy, efficiency and interpretability. To compare the models fairly, we have used Optuna to optimize the hyperparameters for training each model. Our previous results showed that random forest might be the best non-deep learning model for cyberattack detection. In this paper, we investigate the performance of deep learning, and see if it is possible to improve the accuracy regardless of the computational cost. To make the results more reliable, a new test set is added to compare the best models. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Intrusion DetectionMachine LearningRandom Forest ClassifiersHyperparameter OptimizationRecurrent Neural NetworkLong Short-Term Memory |
| Paper # | IT2022-22 |
| Date of Issue | 2022-07-14 (IT) |
| Conference Information | |
| Committee | IT |
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| Conference Date | 2022/7/21(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okayama University of Science |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Freshman session, General |
| Chair | Tetsuya Kojima(Tokyo Kosen) |
| Vice Chair | Yasuyuki Nogami(Okayama Univ.) |
| Secretary | Yasuyuki Nogami(Saitamai Univ.) |
| Assistant | Takayuki Nozaki(Yamaguchi Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Information Theory |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A study on deep learning-based cyber attack detection |
| Sub Title (in English) | |
| Keyword(1) | Intrusion DetectionMachine LearningRandom Forest ClassifiersHyperparameter OptimizationRecurrent Neural NetworkLong Short-Term Memory |
| 1st Author's Name | Ruei-Fong Hong |
| 1st Author's Affiliation | The University of Aizu(UoA) |
| 2nd Author's Name | Shih-Cheng Horng |
| 2nd Author's Affiliation | Chaoyang University of Technology(CYUT) |
| 3rd Author's Name | Qiangfu Zhao |
| 3rd Author's Affiliation | The University of Aizu(UoA) |
| Date | 2022-07-22 |
| Paper # | IT2022-22 |
| Volume (vol) | vol.122 |
| Number (no) | IT-128 |
| Page | pp.pp.36-41(IT), |
| #Pages | 6 |
| Date of Issue | 2022-07-14 (IT) |