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Mobile Phishing Attacks and Mitigation Techniques

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TLDR
This paper discusses various phishing attacks using mobile devices followed by some discussion on countermeasures to bring more awareness to emerging mobile device-based phishingattacks.
Abstract
Mobile devices have taken an essential role in the portable computer world. Portability, small screen size, and lower cost of production make these devices popular replacements for desktop and laptop computers for many daily tasks, such as surfing on the Internet, playing games, and shopping online. The popularity of mobile devices such as tablets and smart phones has made them a frequent target of traditional web-based attacks, especially phishing. Mobile device-based phishing takes its share of the pie to trick users into entering their credentials in fake websites or fake mobile applications. This paper discusses various phishing attacks using mobile devices followed by some discussion on countermeasures. The discussion is intended to bring more awareness to emerging mobile device-based phishing attacks.

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Proceedings ArticleDOI

Dangerous Skills: Understanding and Mitigating Security Risks of Voice-Controlled Third-Party Functions on Virtual Personal Assistant Systems

TL;DR: Two new attacks are discovered: voice squatting in which the adversary exploits the way a skill is invoked and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill during the user's conversation with the service to steal her personal information.
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Understanding and mitigating the security risks of voice-controlled third-party skills on amazon alexa and google home

TL;DR: This paper implemented two new attacks: voice squatting in which the adversary exploits the way a skill is invoked and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill to steal the user's data or eavesdrop on her conversations.
Journal ArticleDOI

Smishing Detector: A security model to detect smishing through SMS content analysis and URL behavior analysis

TL;DR: A model called ’Smishing Detector’ to identify smishing messages while reducing false-positive results at every possible step is proposed and it is found that this model covers more security aspects as compared to other models.
Proceedings ArticleDOI

Unleashing the Walking Dead: Understanding Cross-App Remote Infections on Mobile WebViews

TL;DR: The contention between the demand for convenient cross-WebView communication and the need for security control on the channel is revealed, and the first step toward building OS-level protection to safeguard this fast-growing technology is made.
References
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Proceedings ArticleDOI

Cantina: a content-based approach to detecting phishing web sites

TL;DR: The design, implementation, and evaluation of CANTINA, a novel, content-based approach to detecting phishing web sites, based on the TF-IDF information retrieval algorithm, are presented.
Proceedings ArticleDOI

An Empirical Analysis of Phishing Blacklists

TL;DR: This paper used 191 fresh phish that were less than 30 minutes old to conduct two tests on eight anti-phishing toolbars and found that two tools using heuristics to complement blacklists caught signicantly more phish initially than those using only blacklists.

SMS Spam Filtering Technique Based on Artificial Immune System

TL;DR: An anti-spam technique based on Artificial Immune System (AIS) for filtering SMS spam messages is proposed, which can classify the SMS spam and ham with accurate compared with Naive Bayesian algorithm.
Posted Content

Hybrid Spam Filtering for Mobile Communication

TL;DR: The trade-off between the accuracy of anti-spam classifiers and the incurring traffic overhead is shown, and it is demonstrated that the hybrid framework is capable of achieving high accuracy regardless of the content-based filtering algorithm being used.
Journal ArticleDOI

Hybrid spam filtering for mobile communication

TL;DR: In this article, the authors proposed an anti-spam framework based on the hybrid of content-based filtering and challenge-response, where a message, that has been classified as uncertain through contentbased filtering, is checked further by sending a challenge to the message sender.
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