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.

We propose a generic knowledge representation framework that supports evolutionary game-theoretic simulations using cognitive agents. The framework allows an experimenter to test a population of such agents via generations to study how specific population behaviours evolve over time. A generation is composed of rounds which can be further divided into encounters according to model-specific requirements. As agents in the population interact, events (caused either by agent actions or by separate environment processes) take place. These events change the environment, changes are then perceived by agents that, in turn, decide to take new actions that affect the environment. This process continues until it is time to evolve a new generation, when strategies of the fittest players are selected for the next generation to start evolving. This evolutionary loop continues until all the terminating conditions of the simulation are met. We use the framework to show how to successfully repeat existing experiments from evolutionary simulations of agent cooperation. Our results validate our framework and pave the way for EVOCOGNISIM, a simulation platform that implements the key aspects of the framework in a systematic manner.

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.

Modern ransomware often generate and manage cryptographic keys on the victim’s machine, giving defenders an opportunity to capture exposed keys and recover encrypted data without paying the ransom. However, recent work has raised the possibility of future enclave-enhanced malware that could avoid such mitigations using emerging support for hardware-enforced secure enclaves in commodity CPUs. Nonetheless, the practicality of such enclave-enhanced malware and its potential impact on all phases of the ransomware lifecyle remain unclear. Given the demonstrated capacity of ransomware authors to innovate in order to better extort their victims (e.g. through the adoption of untraceable virtual currencies and anonymity networks), it is important to better understand the risks involved and identify potential mitigations.

We propose a novel knowledge representation framework called COGNISIM that supports game theoretic simulation experiments using cognitive agents. The framework allows an experimenter to evolve a population of such agents with strategies expressed teleo-reactively as logic programs. When agents encounter each other, events take place in the environment, caused either by agent actions or by environment processes. Such events change the environment’s internal state, and these changes are then observed by agents that, in turn, decide to take new actions that affect the environment. This loop continues until the terminating conditions of the simulation are met. Using this framework, we show how to repeat experiments from the literature based on Axelrod’s tournament. We also evaluate our platform’s performance in efficiently supporting large simulations in game theoretic settings.

Trusted execution environments (TEEs) promise a cost-effective, “lift-and-shift” solution for deploying security-sensitive applications in untrusted clouds. For this, they must support rich, multi-component applications, but a large trusted computing base (TCB) inside the TEE risks that attackers can compromise application security. Fine-grained compartmentalisation can increase security through defense-in-depth, but current solutions either run all software components unprotected in the same TEE, lack efficient shared memory support, or isolate application processes using separate TEEs, impacting performance and compatibility.

A cloud provider that can technically determine tenants' operations may be compelled to disclose such activities by law enforcement agencies (LEAs). The situation gets even more complex when multiple LEAs across different jurisdictions are involved, e.g., because of the distributed locations of cloud servers and data storage. Yet cloud providers typically do not need or want to know about their tenants' activities, other than measuring how such activities incur expenses for using cloud resources. Thus mechanisms should be developed for cloud providers to have sufficient plausible deniability with regards to the processing being carried out by tenants on their platform, in jurisdictions that permit cloud providers to avoid liabilities in this way. Symmetrically, such mechanisms could protect tenants from legal over-reach, for example, when the country in which the cloud provider is incorporated could force disclosure of the processing carried out by cloud tenants. But to what extent can cloud providers acquire plausible deniability? Current discussions regarding risk have focused on data confidentiality and integrity. We argue that processing operations can equally reveal sensitive information-such as trade secrets and business processes-and that for some classes of application both data protection and algorithm protection are necessary. In this paper, we examine the legal and technical motivations for achieving plausible deniability in cloud interactions. We demonstrate the likely performance overhead of using containers secured with technologies such as Intel SGX. Further, we examine the current limitations of our proposed plausible deniability mechanisms, and outline a potential approach for enabling lawful access to enclaves subject to appropriate judicial oversight.

Users of online services such as messaging, code hosting and collaborative document editing expect the services to uphold the integrity of their data. Despite providers' best efforts, data corruption still occurs, but at present service integrity violations are excluded from SLAs. For providers to include such violations as part of SLAs, the competing requirements of clients and providers must be satisfied. Clients need the ability to independently identify and prove service integrity violations to claim compensation. At the same time, providers must be able to refute spurious claims.

In an edge deployment model, Internet-of-Things (IoT) applications, e.g. for building automation or video surveillance, must process data locally on IoT devices without relying on permanent connectivity to a cloud backend. The ability to harness the combined resources of multiple IoT devices for computation is influenced by the quality of wireless network connectivity. An open challenge is how practical edge-based IoT applications can be realised that are robust to changes in network bandwidth between IoT devices, due to interference and intermittent connectivity. We present Frontier , a distributed and resilient edge processing platform for IoT devices. The key idea is to express data-intensive IoT applications as continuous data-parallel streaming queries and to improve query throughput in an unreliable wireless network by exploiting network path diversity : a query includes operator replicas at different IoT nodes, which increases possible network paths for data. Frontier dynamically routes stream data to operator replicas based on network path conditions. Nodes probe path throughput and use backpressure stream routing to decide on transmission rates, while exploiting multiple operator replicas for data-parallelism. If a node loses network connectivity, a transient disconnection recovery mechanism reprocesses the lost data. Our experimental evaluation of Frontier shows that network path diversity improves throughput by 1.3×−2.8×for different IoT applications, while being resilient to intermittent network connectivity.

Bugs in the authorisation logic of web applications can expose the data of one user to another. Such data disclosure vulnerabilities are common---they can be caused by a single omitted access control check in the application. We make the observation that, while the implementation of the authorisation logic is complex and therefore error-prone, most web applications only use simple access control models, in which each piece of data is accessible by a user or a group of users. This makes it possible to validate the correct operation of the authorisation logic externally, based on the observed data in HTTP traffic to and from an application.

Sensor networks are composed of a large number of densely deployed sensors/actuators. Routing protocols are faced with the challenge of delivering data to sinks through multihop routes, in the presence of energy constrained sensor nodes. We present an energy-aware event dissemination protocol for mobile wireless sensor networks. In our proposed model each sink pro-actively constructs a redundant tree in the network to provide reliable delivery of events in the face of dynamic changes and mobility. Scalability is achieved by minimizing the number of participating nodes on the tree, while maintaining high coverage.