This paper presents a mobile app that integrates data from various sources, such as the city open data portal, the 911 emergency service, crowdsourced information from citizens in nearby areas, and user's personal data. Nevertheless, the widespread use of mobile devices raises relevant opportunities in the prevention of security problems and the protection of public safety.
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Unfortunately, in some communities, particularly in developing countries, robberies, murders, kidnappings, gender violence, among others, are common on a daily basis, and this trend does not seem to diminish. Consequently, smart devices are secured against any runtime vulnerabilities that may surface due to human factors.Ĭurrently, urban centers are affected by insecurity to a lesser or a greater extent, being public safety a pending issue on the agenda of most governments. Thus, our model dynamically fulfills the security criteria of the security-sensitive applications and revokes resources access permission given to them, until features reliability is not set to a secure level. The resource access permissions (e.g., ACCESS INTERNET and ACCESS NETWORK STATE) given to an application requiring higher security are revoked in case users configure less reliable features (e.g., open WIFI or HOTSPOT) on their smart devices. In this paper, we propose a risk-driven model that determines features reliability at runtime by monitoring users' features usage patterns. Conventional security mechanisms mainly focus on preventing and monitoring mal-ware, but they do not perform the runtime vulnerabilities assessment while users use their smart devices. However, un-trustworthy features configuration could mount severe risks towards the protection and integrity of data and assets residing on smart devices or to perform security-sensitive activities on smart devices. Typically, smart devices provide multiple configurable features, e.g., user authentication settings, network selection, application installation, communication interfaces, etc., which users can configure according to their needs and convenience. Human error exploitation could entail unfavorable consequences to smart device users. Consequently, smart devices are secured against any runtime vulnerabilities that may surface due to human factors. Thus, our model dynamically fulfills the security criteria of the security-sensitive applications and revokes resources access permission given to them, until features reliability is set to a secure level. The resource access permissions (e.g., ACCESS_INTERNET and ACCESS_NETWORK_STATE) given to an application requiring higher security are revoked in case users configure less reliable features (e.g., open WIFI or HOTSPOT) on their smart devices. In this paper, we propose a risk-driven model that determines features reliability at runtime by monitoring users’ features usage patterns. Conventional security mechanisms mainly focus on preventing and monitoring malware, but they do not perform the runtime vulnerabilities assessment while users use their smart devices. However, untrustworthy features configuration could mount severe risks towards the protection and integrity of data and assets residing on smart devices or to perform security-sensitive activities on smart devices. Typically, smart devices provide multiple configurable features, e.g., user authentication settings, network selection, application installation, communication interfaces, etc., which users can configure according to their need and convenience. Human errors exploitation could entail unfavorable consequences to smart device users. Our goal is to design an operating system framework that hinders malicious activities and thus protects user resources.
#Pdfsam installer malware software
It also provides APIs to aid anti-virus software and third-party applications to leverage the functionalities of the framework.
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It has components to enforce laws (prevent) and perform policing (monitor and control). The framework acts as the government of the city and treats the applications as citizens. The core idea of the framework resembles a smart city. In this paper, we propose a Secure Anti-Malware framework (SAM) for smartphone operating systems to prevent malicious activities. Traditional desktop anti-virus software are not very effective in smartphones due to the restrictive security model and they are heavily dependent on their definition updates. Consequently, security attacks on smartphone platforms have also increased significantly. Users also store sensitive personal information on their smartphones and perform financial transactions. Businesses now offer services through smartphones. Smartphones have become an integral part of our daily life.