| Paper Abstract and Keywords |
| Presentation |
2023-10-11 14:30
[Poster Presentation]
Detecting Poisoning Attacks Using Adversarial Examples in Deep Phishing Detection
Koko Nishiura, Tomotaka Kimura, Jun Cheng (Doshisha Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
In recent years, the convenience of online services has greatly improved, but the number of phishing scams has skyrocketed. To prevent phishing scams, detection systems based on deep learning have been proposed and shown to be effective. However, deep learning has a vulnerability of intentionally causing misclassification. In this presentation, we examine countermeasures against backdoor attacks that induce misclassification for input data containing specific triggers. Specifically, we generate noise using the AE (Adversarial Example) method from a detection system undergoing a backdoor attack, and compare the perturbed data with noise and the judgment labels of the perturbed data. When generating perturbation data, the area where noise is generated is restricted and the data is evaluated by shifting the position of the restricted area. If the trigger area contains noise, the trigger is broken and will not be misclassified, but if it does not contain noise, it will be misclassified. Comparing the judgment labels of the perturbation data with the perturbation data, the judgment label agreement differs depending on the trigger position, so the point where the value becomes abnormal can be determined to be the trigger. Furthermore, detection of a trigger indicates that the deep learning model used is under backdoor attack. In this presentation, we will evaluate the feasibility of detecting backdoor attacks and triggers using a dataset containing data with embedded triggers, assuming a real environment. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Adversarial Examples / Phishing / Deep Learning / Poisoning Attack / / / / |
| Reference Info. |
IEICE Tech. Rep. |
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| Conference Information |
| Committee |
MIKA |
| Conference Date |
2023-10-10 - 2023-10-12 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Okinawa Jichikaikan |
| Topics (in Japanese) |
(See Japanese page) |
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| Paper Information |
| Registration To |
MIKA |
| Conference Code |
2023-10-MIKA |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Detecting Poisoning Attacks Using Adversarial Examples in Deep Phishing Detection |
| Sub Title (in English) |
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| Keyword(1) |
Adversarial Examples |
| Keyword(2) |
Phishing |
| Keyword(3) |
Deep Learning |
| Keyword(4) |
Poisoning Attack |
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| 1st Author's Name |
Koko Nishiura |
| 1st Author's Affiliation |
Doshisha University (Doshisha Univ.) |
| 2nd Author's Name |
Tomotaka Kimura |
| 2nd Author's Affiliation |
Doshisha University (Doshisha Univ.) |
| 3rd Author's Name |
Jun Cheng |
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Doshisha University (Doshisha Univ.) |
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| Date Time |
2023-10-11 14:30:00 |
| Presentation Time |
90 minutes |
| Registration for |
MIKA |
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