Abstract Difficulties with accessing device content or even the device itself can seriously hamper smartphone forensics. Mobile cloud storage, which extends on-device capacity, provides an avenue for a forensic collection process that does not require physical access to the device. Rather, it is possible to remotely retrieve credentials from a device of interest through undercover operations, followed by live cloud forensics. While technologically appealing, this approach raises concerns with evidence preservation, ranging from the use of malware-like operations, to linking the collected evidence with the physically absent smartphone, and possible mass surveillance accusations. In this paper, we propose a solution to ease these concerns by employing hardware security modules to provide for controlled live cloud forensics and tamper-evident access logs. A Google Drive-based proof of concept, using the SEcube hardware security module, demonstrates that D-Cloud-Collector is feasible whenever the performance penalty incurred is affordable.

In recent years the PC has been replaced by mobile devices for many security sensitive operations, both from a privacy and a financial standpoint. While security mechanisms are deployed at various levels, these are frequently put under strain by previously unseen malware. An additional protection layer capable of novelty detection is therefore needed. In this work we propose SpotCheck, an anomaly detector intended to run on Android devices. It samples app executions and submits suspicious apps to more thorough processing by malware sandboxes. We compare Kernel Principal Component Analysis (KPCA) and Variational Autoencoders (VAE) on app execution representations based on the well-known system call traces, as well as a novel approach based on memory dumps. Results show that when using VAE, SpotCheck attains a level of effectiveness comparable to what has been previously achieved for network anomaly detection. Interestingly this is also true for the memory dump approach, relinquishing the need for continuous app monitoring.

Abstract Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.

The first international Competition on Runtime Verification (CRV) was held in September 2014, in Toronto, Canada, as a satellite event of the 14th international conference on Runtime Verification (RV’14). The event was organized in three tracks: (1) offline monitoring, (2) online monitoring of C programs, and (3) online monitoring of Java programs. In this paper, we report on the phases and rules, a description of the participating teams and their submitted benchmark, the (full) results, as well as the lessons learned from the competition.

The use of runtime verification, as a lightweight approach to guarantee properties of systems, has been increasingly employed on real-life software. In this paper, we present the tool LARVA, for the runtime verification of properties of Java programs, including real-time properties. Properties can be expressed in a number of notations, including timed-automata enriched with stopwatches, Lustre, and a subset of the duration calculus. The tool has been successfully used on a number of case-studies, including an industrial system handling financial transactions. LARVA also performs analysis of real-time properties, to calculate, if possible, an upper-bound on the memory and temporal overheads induced by monitoring. Moreover, through property analysis, LARVA assesses the impact of slowing down the system through monitoring, on the satisfaction of the properties.