The dynamic nature of cloud environments has amplified the challenges of defending against Advanced Persistent Threats (APTs), which exploit the complexity and interconnectedness of modern infrastructures. Existing datasets fail to adequately address the unique requirements of cloud-native systems, particularly those leveraging system provenance graphs for comprehensive analysis. In this work, we present CloudAPT, the first dataset to utilize system provenance graphs for capturing APT behaviors in Kubernetes-based cloud environments. The dataset spans eight days, encompassing the complete APT lifecycle: reconnaissance, initial compromise, privilege escalation, lateral movement, data exfiltration, and covering tracks, while integrating realistic, human-driven user interactions. By centralizing activities on a single worker VM, the dataset ensures granular and transparent data collection, avoiding fragmentation and providing a holistic view of attacker strategies and system responses. CloudAPT includes provenance graph data, cluster logs, and application-level data, offering deep insights into interactions within cloud-native systems. This dataset serves as a foundational resource for developing and benchmarking advanced detection mechanisms and security solutions tailored specifically to the complexities of cloud environments.

Provenance graphs are structured audit logs that describe the history of a system’s execution. Recent studies have explored a variety of techniques to analyze provenance graphs for automated host intrusion detection, focusing particularly on advanced persistent threats. Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?), attack agnosticity (can PIDSes detect novel attacks without a priori knowledge of attack characteristics?), timeliness (can PIDSes efficiently monitor host systems as they run?), and attack reconstruction (can PIDSes distill attack activity from large provenance graphs so that sysadmins can easily understand and quickly respond to system intrusion?). We present Kairos, the first PIDS that simultaneously satisfies the desiderata in all four dimensions, whereas existing approaches sacrifice at least one and struggle to achieve comparable detection performance.Kairos leverages a novel graph neural network based encoder-decoder architecture that learns the temporal evolution of a provenance graph’s structural changes to quantify the degree of anomalousness for each system event. Then, based on this fine-grained information, Kairos reconstructs attack footprints, generating compact summary graphs that accurately describe malicious activity over a stream of system audit logs. Using state-of-the-art benchmark datasets, we demonstrate that Kairos outperforms previous approaches.

The security of container-based microservices relies heavily on the isolation of operating system resources that is provided by namespaces. However, vulnerabilities exist in the isolation of containers that may be exploited by attackers to gain access to the host. These are commonly referred to as container escape attacks. While prior work has identified vulnerabilities in namespace isolation, no general container escape detection and warning system has been presented. We present Paced, a novel, realtime system to detect container-escape attacks. We define what constitutes a cross-namespace event and how such events can be used to detect a container escape attack. We develop a provenance-based approach to isolate cross-namespace events and propose a rule—privileged_flow—to detect attacks on Docker and Kubernetes environments. We evaluate our detection method on a suite of contemporary CVEs with container escape exploits, bad container configurations, and benchmarks. Paced achieves near-perfect accuracy with no false negatives. We release our implementation and datasets as free, open-source software.

Despite the wide usage of container-based cloud computing, container auditing for security analysis relies mostly on built-in host audit systems, which often lack the ability to capture high-fidelity container logs. State-of-the-art reference-monitor-based audit techniques greatly improve the quality of audit logs, but their system-wide architecture is too costly to be adapted for individual containers. Moreover, these techniques typically require extensive kernel modifications, making it difficult to deploy in practical settings.

Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

One of the key challenges for data analytics deployment is configuration tuning. The existing approaches for configuration tuning are expensive and overlook the dynamic characteristics of the analytics environment (i.e. frequent changes in workload due to receiving evolving input sizes or change in the underlying cluster environment). Such workload/environment changes can cause significant performance degradation, with retuning the configuration to accommodate those changes can yield up to 85% potential execution time saving. We propose SimTune, an approach that accommodates such changes through efficient configuration tuning. SimTune combines workload characterization and Multitask Bayesian optimization to identify similarity across workloads and accelerate finding near-optimal configurations. Our experimental results show that SimTune reduces the search time for finding close-to-optimal configurations by 56-73% (at the median) when compared to existing state-of-the-art techniques. This means that the amortization of the tuning cost happens significantly faster, enabling practical tuning in the rapidly changing environment of distributed analytics.

The rise of IoT has led to large volumes of personal data being produced at the network's edge. Most IoT applications process data in the cloud raising concerns over privacy and security. As many IoT applications are event-based and are implemented on cloud-based, serverless platforms, we've seen a number of proposals to deploy serverless solutions at the edge to address concerns over data transfer. However, conventional serverless platforms use container technology to run user-defined functions. Containers introduce their own issues regarding security - due to a large trusted computing base -, and performance issues including long initialisation times. Additionally, OpenWhisk a popular and widely used container-based serverless platform available for edge devices perform relatively poorly as we demonstrate in our evaluation. In this paper, we propose to investigate unikernel as a solution to build serverless platform at the edge, addressing in particular performance and security concerns. We present UniFaaS, a prototype edge-serverless platform which leverages unikernels - tiny library single-address-space operating systems that only contain the parts of the OS needed to run a given application - to execute functions. The result is a serverless platform with extremely low memory and CPU footprints, and excellent performance. UniFaaS has been designed to be deployed on low-powered single-board computer devices, such as Raspberry Pi or Arduino, without compromising on performance.

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.

This experimental study presents a number of issues that pose a challenge for practical configuration tuning and its deployment in data analytics frameworks. These issues include: 1) the assumption of a static workload or environment, ignoring the dynamic characteristics of the analytics environment (e.g., increase in input data size, changes in allocation of resources). 2) the amortization of tuning costs and how this influences what workloads can be tuned in practice in a cost-effective manner. 3) the need for a comprehensive incremental tuning solution for a diverse set of workloads. We adapt different ML techniques in order to obtain efficient incremental tuning in our problem domain, and propose Tuneful, a configuration tuning framework. We show how it is designed to overcome the above issues and illustrate its applicability by running a wide array of experiments in cloud environments provided by two different service providers.

The execution of distributed data processing workloads (such as those running on top of Hadoop or Spark) in cloud environments presents a unique opportunity to explore multiple trade-offs between elasticity (and types of resources being allocated), overall runtime and total costs. However, beyond high-level constraints and objectives, it's not the end-users who should be mainly concerned with those optimizations, but the cloud providers. They have both the vantage point to collect actionable information, economies of scale and position to adjust parameters when dynamic conditions change, in order to fulfil SLOs that go beyond classic measures of latency and throughput. This is at odds with the existing approach of making software (including the interfaces to the cloud and the processing frameworks) as configurable as possible. We propose that rather than configurability, self-tunability (or the illusion of it as far as the end-user is concerned) is a better long-term goal.

System level provenance is of widespread interest for applications such as security enforcement and information protection. However, testing the correctness or completeness of provenance capture tools is challenging and currently done manually. In some cases there is not even a clear consensus about what behavior is correct. We present an automated tool, ProvMark, that uses an existing provenance system as a black box and reliably identifies the provenance graph structure recorded for a given activity, by a reduction to subgraph isomorphism problems handled by an external solver. ProvMark is a beginning step in the much needed area of testing and comparing the expressiveness of provenance systems. We demonstrate ProvMark's usefuless in comparing three capture systems with different architectures and distinct design philosophies.

Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security. Digital provenance provides a detailed history of the flow of information within a computing system, connecting suspicious events to their root causes. Although existing provenance-based auditing techniques provide value in forensic analysis, they assume that such analysis takes place only retrospectively. Such post-hoc analysis is insufficient for realtime security applications; moreover, even for forensic tasks, prior provenance collection systems exhibited poor performance and scalability, jeopardizing the timeliness of query responses. We present CamQuery, which provides inline, realtime provenance analysis, making it suitable for implementing security applications. CamQuery is a Linux Security Module that offers support for both userspace and in-kernel execution of analysis applications. We demonstrate the applicability of CamQuery to a variety of runtime security applications including data loss prevention, intrusion detection, and regulatory compliance. In evaluation, we demonstrate that CamQuery reduces the latency of realtime query mechanisms, while imposing minimal overheads on system execution. CamQuery thus enables the further deployment of provenance-based technologies to address central challenges in computer security.

Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system's behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.

Unikernels are a rapidly emerging technology in the world of cloud computing. Unikernels build on research into library operating systems to deliver smaller, faster and more secure virtual machines, specifically optimised for a single application service. These features are especially useful in cost or resource constrained environments. However, as with any new technology, early adopters need to master many technical details, and understand many aspects of the mechanisms used to build and deploy unikernels. Both of these factors may slow adoption rates. In this paper, we present our initial experiments into the use of an approach for building unikernels that is accessible to those whose technical expertise is focused on web development. We present PHP2Uni: a tool chain that takes a website built from PHP files-PHP remains the most widely used web language-and builds a resource-efficient unikernel image from them, while requiring little knowledge of the underlying operating system software complexity.

A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed bythose applications. They also require flexible data sharingwith other applications, often across the traditional cloud-isolation boundaries; for example, when government, consisting of different departments, provides services to its citizens through a common platform. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows;a crucial issue once data has left its owner's control by cloud-hosted applications andwithin cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of clouddeployed IFC for enforcingowners' data flow policy with regard to protection and sharing, aswell as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency and offers system-wide visibility over data flows. This helps those responsible to meet their data management obligations, providing evidence of compliance, and aids in the identification ofpolicy errors and misconfigurations. We present our IFC model and describe and evaluate our IFC architecture and implementation (CamFlow). This comprises an OS level implementation of IFC with support for application management, together with an IFC-enabled middleware.

With the rapid increase in uptake of cloud services, issues of data management are becoming increasingly prominent. There is a clear, outstanding need for the ability for specified policy to control and track data as it flows throughout cloud infrastructure, to ensure that those responsible for data are meeting their obligations. This paper introduces Information Flow Audit, an approach for tracking information flows within cloud infrastructure. This builds upon CamFlow (Cambridge Flow Control Architecture), a prototype implementation of our model for data-centric security in PaaS clouds. CamFlow enforces Information Flow Control policy both intra-machine at the kernel-level, and inter-machine, on message exchange. Here we demonstrate how CamFlow can be extended to provide data-centric audit logs akin to provenance metadata in a format in which analyses can easily be automated through the use of standard graph processing tools. This allows detailed understanding of the overall system. Combining a continuously enforced data-centric security mechanism with meaningful audit empowers tenants and providers to both meet and demonstrate compliance with their data management obligations.

The usual approach to security for cloud-hosted applications is strong separation. However, it is often the case that the same data is used by different applications, particularly given the increase in data-driven (`big data' and IoT) applications. We argue that access control for the cloud should no longer be application-specific but should be data-centric, associated with the data that can flow between applications. Indeed, the data may originate outside cloud services from diverse sources such as medical monitoring, environmental sensing etc. Information Flow Control (IFC) potentially offers data-centric, system-wide data access control. It has been shown that IFC can be provided at operating system level as part of a PaaS offering, with an acceptable overhead.

To realize the broad vision of pervasive computing, underpinned by the “Internet of Things” (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends toward the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy, and personal safety risks that arise beyond these subsystems; i.e., from the wide-scale, cross-platform openness that cloud services bring to IoT. In this paper, we focus on security considerations for IoT from the perspectives of cloud tenants, end-users, and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyze the current state of cloud-supported IoT to make explicit the security considerations that require further work.

Concern about data leakage is holding back more widespread adoption of cloud computing by companies and public institutions alike. To address this, cloud tenants/applications are traditionally isolated in virtual machines or containers. But an emerging requirement is for cross-application sharing of data, for example, when cloud services form part of an IoT architecture. Information Flow Control (IFC) is ideally suited to achieving both isolation and data sharing as required. IFC enhances traditional Access Control by providing continuous, data-centric, cross-application, end-to-end control of data flows. However, large-scale data processing is a major requirement of cloud computing and is infeasible under standard IFC. We present a novel, enhanced IFC model that subsumes standard models. Our IFC model supports `Big Data' processing, while retaining the simplicity of standard IFC and enabling more concise, accurate and maintainable expression of policy.

There is a clear, outstanding need for new security mechanisms that allow data to be managed and controlled within the cloud-enabled Internet of Things. Towards this, we propose an approach based on Information Flow Control (IFC) that allows: (1) the continuous, end-to-end enforcement of data flow policy, and (2) the generation of provenance-like audit logs to demonstrate policy adherence and contractual/regulatory compliance. Further, we discuss the role of Trusted Platform Modules (TPMs) in supporting such a system, by providing hardware roots of trust. TPMs can be leveraged to validate software configurations, including the IFC enforcement mechanism, both in the cloud and externally via remote attestation.

The adoption of cloud computing is increasing and its use is becoming widespread in many sectors. As cloud service provision increases, legal and regulatory issues become more significant. In particular, the international nature of cloud provision raises concerns over the location of data and the laws to which they are subject. In this paper we investigate Information Flow Control (IFC) as a possible technical solution to expressing, enforcing and demonstrating compliance of cloud computing systems with policy requirements inspired by data protection and other laws. We focus on geographic location of data, since this is the paradigmatic concern of legal/regulatory requirements on cloud computing and, to date, has not been met with robust technical solutions and verifiable data flow audit trails.

The need to share data across applications is becoming increasingly evident. Current cloud isolation mechanisms focus solely on protection, such as containers that isolate at the OS-level, and virtual machines that isolate through the hypervisor. However, by focusing rigidly on protection, these approaches do not provide for controlled sharing. This paper presents how Information Flow Control (IFC) offers a flexible alternative. As a data-centric mechanism it enables strong isolation when required, while providing continuous, fine grained control of the data being shared. An IFC-enabled cloud platform would ensure that policies are enforced as data flows across all applications, without requiring any special sharing mechanisms.

Security is an ongoing challenge in cloud computing. Currently, cloud consumers have few mechanisms for managing their data within the cloud provider's infrastructure. Information Flow Control (IFC) involves attaching labels to data, to govern its flow throughout a system. We have worked on kernel-level IFC enforcement to protect data flows within a virtual machine (VM). This paper makes the case for, and demonstrates the feasibility of an IFC-enabled messaging middleware, to enforce IFC within and across applications, containers, VMs, and hosts. We detail how such middleware can integrate with local (kernel) enforcement mechanisms, and highlight the benefits of separating data management policy from application/service-logic.

Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions and although a wealth of law and regulation has emerged, the technical basis for enforcing and demonstrating compliance lags behind. Our Cloud Safety Net project aims to show that Information Flow Control (IFC) can augment existing security mechanisms and provide continuous enforcement of extended. Finer-grained application-level security policy in the cloud. We present FlowK, a loadable kernel module for Linux, as part of a proof of concept that IFC can be provided for cloud computing. Following the principle of policy-mechanism separation, IFC policy is assumed to be expressed at application level and FlowK provides mechanisms to enforce IFC policy at runtime. FlowK's design minimises the changes required to existing software when IFC is provided. To show how FlowK can be integrated with cloud software we have designed and evaluated a framework for deploying IFC-aware web applications, suitable for use in a PaaS cloud.

Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions. Information Flow Control (IFC) is a well understood Mandatory Access Control methodology. The earliest IFC models targeted security in a centralised environment, but decentralised forms of IFC have been designed and implemented, often within academic research projects. As a result, there is potential for decentralised IFC to achieve better cloud security than is available today. In this paper we describe the properties of cloud computing-Platform-as-a-Service clouds in particular-and review a range of IFC models and implementations to identify opportunities for using IFC within a cloud computing context. Since IFC security is linked to the data that it protects, both tenants and providers of cloud services can agree on security policy, in a manner that does not require them to understand and rely on the particulars of the cloud software stack in order to effect enforcement.