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Mobile Augmented Reality (MAR) is a portable, powerful, and suitable technology that integrates digital content, e.g., 3D virtual objects, into the physical world, which not only has been implemented for multiple intents such as shopping, entertainment, gaming, etc., but it is also expected to grow at a tremendous rate in the upcoming years. Unfortunately, the applications that implement MAR, hereby referred to as MAR-Apps, bear security issues, which have been imaged in worldwide incidents such as robberies, which has led authorities to ban MAR-Apps at specific locations. Existing problems with MAR-Apps can be classified into three categories: first, Space Invasion, which implies the intrusive modification through MAR of sensitive spaces, e.g., hospitals, memorials, etc. Second, Space Affectation, which involves the degradation of users' experience via interaction with undesirable MAR or malicious entities. Finally, MAR-Apps mishandling sensitive data leads to Privacy Leaks. To alleviate these concerns, we present an approach for Policy-Governed MAR-Apps, which allows end-users to fully control under what circumstances, e.g., their presence inside a given sensitive space, digital content may be displayed by MAR-Apps. Through SpaceMediator, a proof-of-concept MAR-App that imitates the well-known and successful MAR-App Pokemon GO, we evaluated our approach through a user study with 40 participants, who recognized and prevented the issues just described with success rates as high as 92.50%. Furthermore, there is an enriched interest in Policy-Governed MAR-Apps as 87.50% of participants agreed with it, and 82.50% would use it to implement content-based restrictions in MAR-Apps These promising results encourage the adoption of our solution in future MAR-Apps. 

We investigate the feasibility of targeted privacy attacks using only information available in physical channels of LTE mobile networks and propose three privacy attacks to demonstrate this feasibility: mobile-app fingerprinting attack, history attack, and correlation attack. These attacks can reveal the geolocation of targeted mobile devices, the victim's app usage patterns, and even the relationship between two users within the same LTE network cell. An attacker also may launch these attacks stealthily by capturing radio signals transmitted over the air, using only a passive sniffer as equipment. To ensure the impact of these attacks on mobile users' privacy, we perform evaluations in both laboratory and real-world settings, demonstrating their practicality and dependability. Furthermore, we argue that these attacks can target not only 4G/LTE but also the evolving 5G standards. 

Android applications are extremely popular, as they are widely used for banking, social media, e-commerce, etc. Such applications typically leverage a series of Permissions, which serve as a convenient abstraction for mediating access to security-sensitive functionality within the Android Ecosystem, e.g., sending data over the Internet. However, several malicious applications have recently deployed attacks such as data leaks and spurious credit card charges by abusing the Permissions granted initially to them by unaware users in good faith. To alleviate this pressing concern, we present DyPolDroid, a dynamic and semi-automated security framework that builds upon Android Enterprise, a device-management framework for organizations, to allow for users and administrators to design and enforce so-called Counter-Policies, a convenient user-friendly abstraction to restrict the sets of Permissions granted to potential malicious applications, thus effectively protecting against serious attacks without requiring advanced security and technical expertise. Additionally, as a part of our experimental procedures, we introduce Laverna, a fully operational application that uses permissions to provide benign functionality at the same time it also abuses them for malicious purposes. To fully support the reproducibility of our results, and to encourage future work, the source code of both DyPolDroid and Laverna is publicly available as open-source. 

Phishing is a ubiquitous and increasingly sophisticated online threat. To evade mitigations, phishers try to ""cloak"" malicious content from defenders to delay their appearance on blacklists, while still presenting the phishing payload to victims. This cat-and-mouse game is variable and fast-moving, with many distinct cloaking methods---we construct a dataset identifying 2,933 real-world phishing kits that implement cloaking mechanisms. These kits use information from the host, browser, and HTTP request to classify traffic as either anti-phishing entity or potential victim and change their behavior accordingly. In this work we present SPARTACUS, a technique that subverts the phishing status quo by disguising user traffic as anti-phishing entities. These intentional false positives trigger cloaking behavior in phishing kits, thus hiding the malicious payload and protecting the user without disrupting benign sites. To evaluate the effectiveness of this approach, we deployed SPARTACUS as a browser extension from November 2020 to July 2021. During that time, SPARTACUS browsers visited 160,728 reported phishing URLs in the wild. Of these, SPARTACUS protected against 132,274 sites (82.3%). The phishing kits which showed malicious content to SPARTACUS typically did so due to ineffective cloaking---the majority (98.4%) of the remainder were detected by conventional anti-phishing systems such as Google Safe Browsing or VirusTotal, and would be blacklisted regardless. We further evaluate SPARTACUS against benign websites sampled from the Alexa Top One Million List for impacts on latency, accessibility, layout, and CPU overhead, finding minimal performance penalties and no loss in functionality.