The recent advent of serverless applications has created a need for static analysis tools to analyse them. However, the event-driven architecture of serverless applications, along with the black-box nature of the services they invoke, make static analysis challenging. In this work, we propose a novel approach to statically analysing serverless applications, with a focus on the identification of data flows that can lead to code injection and information leakage. To reach our goal, we first design a new suite of microbenchmarks, which we publicly release. The microbenchmarks are based on documented serverless-specific vulnerabilities and the characterization of an existing dataset. We then introduce our static analysis approach and show how it can factor in the effect of platform services and eventtriggered code execution by extracting relevant information from both infrastructure and application code. This information is used to obtain a synchronous equivalent of the underlying asynchronous system, which can be inspected with a general-purpose static analysis tool. Preliminary evaluation results using a prototype implementation of our approach and the microbenchmark suite confirm the potential of our analysis technique.

Anti-virus (AV) software is widely recognized as one of the most important defensive tools against malware. Although historically many Linux users considered this operating system to be malware-free, recent research suggests that Linux malware is on the rise. However, to date there has not been a comprehensive observational study on the effectiveness of modern Linux AVs.In this work, we evaluate a range of Linux AVs using a dataset of 43,553 Linux malware samples, conducting our analysis over a period of ten months to identify possible regression effects. We measure the detection rates of Linux AVs available in our local test environment and on an online malware scanning service. Furthermore, we perform a Linux malware capability analysis using the open-source tool CAPA. Overall, the results of this work show that Linux AVs’ signature databases are not well maintained by AV vendors, and that several Linux AVs are affected by regression. In addition, our capability analysis suggests that malware authors are trying to further specialize existing approaches for evading AV software rather than developing new capabilities.

Electronic textile research often centers on the concept of introducing electronics to apparel such as shirts jackets, gloves, and health vests. Another less researched concept incorporates electronics into large textile surfaces such as carpets and upholstery. In this paper we explore methods and challenges of building a large surface area electronic textile floor for cooperative mobile device interaction. We systematically construct both a 100ft by 50ft textile simulation and a 3ft by 8ft working prototype using readily available materials. We introduce the broadly applicable embedded workload of human gait tracking as a means to defining the requirements for the textile physicals, networked computing node, and distributed execution environment subsystems. Through this effort we begin to establish a working model for combining inexpensive electronic textiles with a scalable execution environment that supports mobile device to floor interactions.

Handheld devices are becoming an integral part of every day life in the home, office, and on the road. Furthermore, mobile devices are a key ingredient in the pervasive environments. To be effective, those devices need to be thrifty in power consumption, attentive to user intentions and environment changes and should be easily managed. Several sensor based applications such as GPS mapping, finger print based authentication, and accelerometer based hard disk protection are becoming popular in small hand held devices. In this paper, a formalized architecture is proposed to allow systematic access and use of low cost sensors that are being built into small hand held systems for various end user functions. Preliminary results of the impact of using sensors to save power in the mobile architecture based on various policies are shown. The low power platform used for the experiments and measurement of results is a concept platform developed at Intel® Labs. The sensor-board used in this project is made at ARCES Labs (University of Bologna), funded by *Ducati Sistemi S.p.A. and MIUR.