ICACT20260287 Question.1
Questioner: hovhannes.movsisyan@rau.am 2026-02-09 오후 3:34:18 |
ICACT20260287 Answer.1
Answer by Auhor nahar@aiub.edu 2026-02-09 오후 3:34:18
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| What can you say about the false positive rate of your detection method? |
Our system doesn’t flag/detect users based on just one sign. It checks multiple behaviors and verifies them before acting, and it uses soft control instead of instant blocking, so normal users arent going to be wrongly detected. So, we can say, the false positive rate is expected to be low. |
ICACT20260287 Question.2
Questioner: hovhannes.movsisyan@rau.am 2026-02-09 오후 3:36:02 |
ICACT20260287 Answer.2
Answer by Auhor nahar@aiub.edu 2026-02-09 오후 3:36:02
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| How do false positives affect network performance and user experience? For example, can misclassification impact legitimate users? |
Yes, False positives can hurt both network performance and user experience. If a normal user is wrongly detected as an attacker, the system may slow down their connection, limit their transmissions, or even temporarily block them. This can cause delay, low speed, or disconnections. By which users maybe frustrated feel the network unreliable. Why false positives matter: Misclassification directly impacts legitimate users, even though they did nothing wrong. A good detection system keeps false positives low so normal users can use the network smoothly without being disturbed. |
ICACT20260287 Question.3
Questioner: arjunvirvashisht@gmail.com 2026-02-10 오후 3:06:10 |
ICACT20260287 Answer.3
Answer by Auhor nahar@aiub.edu 2026-02-10 오후 3:06:10
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| Was the behavior data for this study collected from a real campus network or generated synthetically? |
As mentioned in the Results section, the proposed algorithm has not yet been deployed on physical devices for data collection. To obtain the data and assess the algorithm’s accuracy, we conducted experiments in a simulated environment using Google Colab and collected the resulting data from these simulations. |
