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 Andalucía 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