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Opinion Model Based Security Reputation Enabling Cloud Broker Architecture
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A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
Evolution Oriented Monitoring oriented to Security Properties for Cloud Applications
Internet is changing from an information space to a dynamic computing
space. Data distribution and remotely accessible software
services, dynamism, and autonomy are prime attributes. Cloud technology
offers a powerful and fast growing approach to the provision
of infrastructure (platform and software services) avoiding the high
costs of owning, operating, and maintaining the computational
infrastructures required for this purpose. Nevertheless, cloud technology
still raises concerns regarding security, privacy, governance,
and compliance of data and software services offered through it.
Concerns are due to the difficulty to verify security properties of
the different types of applications and services available through
cloud technology, the uncertainty of their owners and users about
the security of their services, and the applications based on them,
once they are deployed and offered through a cloud. This work
presents an innovative and novel evolution-oriented, cloud-specific
monitoring model (including an architecture and a language) that
aim at helping cloud application developers to design and monitor
the behavior and functionality of their applications in a cloud
environment.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
User-centric Privacy Engineering for the Internet of Things
User privacy concerns are widely regarded as a key obstacle to the success of
modern smart cyber-physical systems. In this paper, we analyse, through an
example, some of the requirements that future data collection architectures of
these systems should implement to provide effective privacy protection for
users. Then, we give an example of how these requirements can be implemented in
a smart home scenario. Our example architecture allows the user to balance the
privacy risks with the potential benefits and take a practical decision
determining the extent of the sharing. Based on this example architecture, we
identify a number of challenges that must be addressed by future data
processing systems in order to achieve effective privacy management for smart
cyber-physical systems.Comment: 12 Page
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Authentication of smartphone users is important because a lot of sensitive
data is stored in the smartphone and the smartphone is also used to access
various cloud data and services. However, smartphones are easily stolen or
co-opted by an attacker. Beyond the initial login, it is highly desirable to
re-authenticate end-users who are continuing to access security-critical
services and data. Hence, this paper proposes a novel authentication system for
implicit, continuous authentication of the smartphone user based on behavioral
characteristics, by leveraging the sensors already ubiquitously built into
smartphones. We propose novel context-based authentication models to
differentiate the legitimate smartphone owner versus other users. We
systematically show how to achieve high authentication accuracy with different
design alternatives in sensor and feature selection, machine learning
techniques, context detection and multiple devices. Our system can achieve
excellent authentication performance with 98.1% accuracy with negligible system
overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable
Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap
with arXiv:1703.0352
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