The complex architecture of browser technologies and dynamic characteristics of JavaScript make it difficult to ensure security in client-side web applications. Browser-level security policies alone are not sufficient because it is difficult to apply them correctly and they can be bypassed. As a result, they need to be completed by application-level security policies.In this paper, we survey existing solutions for specifying and enforcing application-level security policies for client-side web applications, and distill a number of desirable features. Based on these features we developed Guardia, a framework for declaratively specifying and dynamically enforcing application-level security policies for JavaScript web applications without requiring VM modifications. We describe Guardia enforcement mechanism by means of JavaScript reflection with respect to three important security properties (transparency, tamper-proofness, and completeness). We also use Guardia to specify and deploy 12 access control policies discussed in related work in three experimental applications that are representative of real-world applications. Our experiments indicate that Guardia is correct, transparent, and tamper-proof, while only incurring a reasonable runtime overhead.

Current browser-level security solutions do not provide a mechanism for information flow control (IFC) policies. As such, they need to be combined with language-based security approaches. Practical implementations for ICF enforcement remains a challenge when the full spectrum of web applications features is taken into account (i.e JavaScript features, web APIs, DOM, portability, performance, etc.). In this work we develop Gifc, a permissive-upgrade-based inlined monitoring mechanism to detect unwanted information flow in web applications. Gifc covers a wide range of JavaScript features that give rise to implicit flows. In contrast to related work, Gifc also handles dynamic code evaluation online, and it features an API function model mechanism that enables information tracking through APIs calls. As a result, Gifc can handle information flows that use DOM nodes as channels of information. We validate Gifc by means of a benchmark suite from literature specifically designed for information flow verification, which we also extend. We compare Gifc qualitatively with respect to closest related work and show that Gifc performs better at detecting unwanted implicit flows.

Context: Static Application Security Testing (SAST) and Runtime Application Security Protection (RASP) are important and complementary techniques used for detecting and enforcing application-level security policies in web applications.Inquiry: The current state of the art, however, does not allow a safe and efficient combination of SAST and RASP based on a shared set of security policies, forcing developers to reimplement and maintain the same policies and their enforcement code in both tools.Approach: In this work, we present a novel technique for deriving SAST from an existing RASP mechanism by using a two-phase abstract interpretation approach in the SAST component that avoids duplicating the effort of specifying security policies and implementing their semantics. The RASP mechanism enforces security policies by instrumenting a base program to trap security-relevant operations and execute the required policy enforcement code. The static analysis of security policies is then obtained from the RASP mechanism by first statically analyzing the base program without any traps. The results of this first phase are used in a second phase to detect trapped operations and abstractly execute the associated and unaltered RASP policy enforcement code.Knowledge: Splitting the analysis into two phases enables running each phase with a specific analysis configuration, rendering the static analysis approach tractable while maintaining sufficient precision.Grounding: We validate the applicability of our two-phase analysis approach by using it to both dynamically enforce and statically detect a range of security policies found in related work. Our experiments suggest that our two-phase analysis can enable faster and more precise policy violation detection compared to analyzing the full instrumented application under a single analysis configuration.Importance: Deriving a SAST component from a RASP mechanism enables equivalent semantics for the security policies across the static and dynamic contexts in which policies are verified during the software development lifecycle. Moreover, our two-phase abstract interpretation approach does not require RASP developers to reimplement the enforcement code for static analysis.

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In recent years the utilization of Recommender Systems has greatly increased, and with it the investigations in this area. Each time investigators strive harder to find techniques and tools that allow improving the performance of said systems. One of the principal problems that investigators face in this field is the continuous change of the users' preferences throughout time, whose analysis supposes an approach to the tastes and preferences of the users. In the present investigation the objective is the design of a model for Recommender Systems in collaborative filtering with temporary dynamics. The proposed model is developed with the utilization of a Hidden Markov Model. This technique is employed with the goal of tracking the continuous change in the users' preferences in time. The proposed solution is described as well as the experimentation carried out to validate the model. The obtained results show a better performance of the proposed model that incorporates the temporary dynamics on the base model that does not have this in mind.