Over the recent decades, numerous evaluations of automated methods for detecting phishing attacks have been reporting stellar detection performances based on empirical evidence. These performances often neglect the adaptive behavior of an adversary seeking to evade detection, yielding uncertainty about their adversarial robustness. This work explores the adversarial robustness of highly influential and recent detection solutions, by assessing their common detection strategies. Following discussions of potential evasion techniques of these strategies, we present examples of techniques that enable evasion through imperceptible perturbations. In order to enable and improve future evaluations for adversarial robustness, a set of design guidelines is proposed.
|Titel||13th USENIX Workshop on Cyber Security Experimentation and Test|
|Forlag||USENIX - The Advanced Computing Systems Association|
|Status||Udgivet - aug. 2020|
|Begivenhed||13th USENIX Workshop on Cyber Security Experimentation and Test - Online|
Varighed: 10 aug. 2020 → 10 aug. 2020
|Konference||13th USENIX Workshop on Cyber Security Experimentation and Test|
|Periode||10/08/2020 → 10/08/2020|