2015/02/24 by Claudio Marforio, Marforio, Claudio, Ramya Jayaram Masti +8 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Spam and Phishing Detection #cs.CR
paper · pdf · doi:10.48550/arxiv.1502.06824
arxiv created 2015/02/24 · openalex publication_date 2015/02/24 · arxiv updated 2015/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Phishing in mobile applications is a relevant threat with successful attacks reported in the wild. In such attacks, malicious mobile applications masquerade as legitimate ones to steal user credentials. In this paper we categorize application phishing attacks in mobile platforms and possible countermeasures. We show that personalized security indicators can help users to detect phishing attacks and have very little deployment cost. Personalized security indicators, however, rely on the user alertness to detect phishing attacks. Previous work in the context of website phishing has shown that users tend to ignore the absence of security indicators and fall victim of the attacker. Consequently, the research community has deemed personalized security indicators as an ineffective phishing detection mechanism. We evaluate personalized security indicators as a phishing detection solution in the context of mobile applications. We conducted a large-scale user study where a significant amount of participants that used personalized security indicators were able to detect phishing. All participants that did not use indicators could not detect the attack and entered their credentials to a phishing application. We found the difference in the attack detection ratio to be statistically significant. Personalized security indicators can, therefore, help phishing detection in mobile applications and their reputation as an anti-phishing mechanism should be reconsidered. We also propose a novel protocol to setup personalized security indicators under a strong adversarial model and provide details on its performance and usability.