CT-AI · Question #103
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker…
The correct answer is A. EDA can help detect the outlier emails from the real emails. The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning: "Testing to detect data poisoning is possible using EDA, as poisoned data may show up as
Question
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks. How could EDA be used to detect this attack?
Options
- AEDA can help detect the outlier emails from the real emails.
- BEDA can detect and remove the false emails.
- CEDA can restrict how many inputs can be provided by unique users.
- DEDA cannot be used to detect the attack.
How the community answered
(35 responses)- A83% (29)
- B9% (3)
- C3% (1)
- D6% (2)
Explanation
The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning: "Testing to detect data poisoning is possible using EDA, as poisoned data may show up as
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