8,739 research outputs found
CamFlow: Managed Data-sharing for Cloud Services
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 by those applications. They also require
flexible data sharing with other applications, often across the traditional
cloud-isolation boundaries; for example, when government provides many related
services for its citizens on a common platform. Similar considerations apply to
the end-users of applications. But in particular, the incorporation of cloud
services within `Internet of Things' architectures is driving the requirements
for both protection and cross-application data sharing.
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 and within 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 cloud-deployed IFC for enforcing
owners' dataflow policy with regard to protection and sharing, as well as
safeguarding against malicious or buggy software. In addition, the audit log
associated with IFC provides transparency, giving configurable system-wide
visibility over data flows. [...]Comment: 14 pages, 8 figure
DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments
With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST
Digital Forensics Investigation Frameworks for Cloud Computing and Internet of Things
Rapid growth in Cloud computing and Internet of Things (IoT) introduces new vulnerabilities that can be exploited to mount cyber-attacks. Digital forensics investigation is commonly used to find the culprit and help expose the vulnerabilities. Traditional digital forensics tools and methods are unsuitable for use in these technologies. Therefore, new digital forensics investigation frameworks and methodologies are required. This research develops frameworks and methods for digital forensics investigations in cloud and IoT platforms
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