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Web applications are exposed to many threats and, despite the best defensive efforts, are often successfully attacked. Reverting the effects of an attack on the state of such an application requires a profound knowledge about the application, to understand what data did the attack corrupt. Furthermore, it requires knowing what steps are needed to revert the effects without modifying legitimate data created by legitimate users. Existing intrusion recovery systems are capable of reverting the effects of the attack but they require modifications to the source code of the application, which may be unpractical. We present Sanare, a pluggable intrusion recovery system designed for web applications that use different data storage systems to keep their state. Sanare does not require any modification to the source code of the application or the web server. Instead, it uses Matchare, a new deep learning scheme we introduce to learn the matches between the HTTP requests and the database statements, file system operations and web services requests that the HTTP requests caused. We evaluated Sanare with three open source web applications: WordPress, GitLab and ownCloud. In our experiments Matchare achieved precision and recall higher than 97.5%.

Many popular mobile applications rely on the Backend-as-a-Service (BaaS) cloud computing model to simplify the development and management of services like data storage, user authentication and notifications. However, vulnerabilities and other issues may lead to malicious operations on the mobile application client-side and malicious requests being sent to the backend, corrupting the state of the application in the cloud. To deal with these attacks after they happen and are successful, it is necessary to remove the immediate effects created by the malicious requests and subsequent effects derived from later requests. In this paper, we present MIRES, an intrusion recovery service for mobile applications based on BaaS. MIRES uses a two-phase recovery process that restores the integrity of the mobile application and minimizes its unavailability. We implemented MIRES in Android and with the Firebase platform and made experiments with 3 mobile applications that showed results of 1000 operations reverted in less than 1 minute and with the mobile application inaccessible only for less than 15 seconds.

Part 2: Connection Security; International audience; TLS ensures confidentiality, integrity, and authenticity of communications. However, design, implementation, and cryptographic vulnerabilities can make TLS communication channels insecure. We need mechanisms that allow the channels to be kept secure even when a new vulnerability is discovered.We present MultiTLS, a middleware based on diversity and tunneling mechanisms that allows keeping communication channels secure even when new vulnerabilities are discovered. MultiTLS creates a secure communication channel through the encapsulation of k TLS channels, where each one uses a different cipher suite. We evaluated the performance of MultiTLS and concluded that it has the advantage of being easy to use and maintain since it does not modify any of its dependencies.

Distributed systems are increasingly important in modern society, often operating on a global scale with stringent dependability requirements. Despite the vast amount of research and the development of techniques to build dependable systems, faults are inevitable as one can witness from regular failures of major providers of IT services. It is therefore fundamental to evaluate distributed systems under different fault patterns and adversarial conditions to assess their high-level behaviour and minimize the occurrence of failures. However, succinctly capturing the system configuration, environment, fault patterns and other variables affecting an experiment is very hard, leading to a reproducibility crisis. In this paper we propose the FaultSee toolkit. The two components of FaultSee are (1) the simple and descriptive FDSL language that captures the system, environment, workload and fault pattern characteristics; and (2) an easy-to-use platform to deploy and run the experiments described by the language. FaultSee allows to precisely describe and reproduce experiments and leads to a better assessment the impact of faults in distributed systems. We showcase the key features of FaultSee by studying the impact of faults with real deployments of Apache Cassandra and BFT-Smart.