Reliable and secure low energy sensed spectrum communication for time critical cloud computing applications

Reliability and security of data transmission and access are of paramount importance to enhance the dependability of time critical remote monitoring systems (e.g. tele-monitoring patients, surveillance of smart grid components). Potential failures for data transmissions include wireless channel unavailability and delays due to the interruptions. Reliable data transmission demands seamless channel availability with minimum delays in spite of interruptions (e.g. fading, denial-of-service attacks). Secure data transmissions require sensed data to be transmitted over unreliable wireless channels with sucient security using suitable encryption techniques. The transmitted data are stored in secure cloud repositories. Potential failures for data access include unsuccessful user authentications due to mis-management of digital identities and insucient permissions to authorize situation specic data access requests. Reliable and secure data access requires robust user authentication and context-dependent authorization to fulll situation specic data utility needs in cloud repositories. The work herein seeks to enhance the dependability of time critical remote monitoring applications, by reducing these failure conditions which may degrade the reliability and security of data transmission or access. As a result of an extensive literature survey, in order to achieve the above said security and reliability, the following areas have been selected for further investigations. The enhancement of opportunistic transmissions in cognitive radio networks to provide greater channel availability as opposed to xed spectrum allocations in conventional wireless networks. Delay sensitive channel access methods to ensure seamless connectivity in spite of multiple interruptions in cognitive radio networks. Energy ecient encryption and route selection mechanisms to enhance both secure and reliable data transmissions. Trustworthy digital identity management in cloud platforms which can facilitate ecient user authentication to ensure reliable access to the sensed remote monitoring data. Context-aware authorizations to reliably handle the exible situation specic data access requests. Main contributions of this thesis include a novel trust metric to select non-malicious cooperative spectrum sensing users to reliably detect vacant channels, a reliable delaysensitive cognitive radio spectrum hand-o management method for seamless connectivity and an energy-aware physical unclonable function based encryption key size selection method for secure data transmission. Furthermore, a trust based identity provider selection method for user authentications and a reliable context-aware situation specic authorization method are developed for more reliable and secure date access in cloud repositories. In conclusion, these contributions can holistically contribute to mitigate the above mentioned failure conditions to achieve the intended dependability of the timecritical remote monitoring applications

Reliable context-aware multi-attribute continuous authentication framework for secure energy utilization management in smart homes

In smart homes, users remotely manage resource utilization tasks and context-aware services using portable devices and mobile communication technologies. Reliability of automated energy consumption management relies upon context-aware continuous authentications of users in executing time-critical tasks. In particular, the contexts of mobility of users and the critical nature of the task are important. Continuous authentication is a robust technique to ensure validity of the authenticity of users over time. Existing continuous authentication techniques do not use the contextual information and dynamic user behavioral characteristics for authentications. We propose a novel context-aware multi-attribute continuous authentication model for secure energy utilization management in smart homes. We use location and the critical nature of the tasks as the contextual information as supporting information for selecting the authentication attributes. We propose novel location and task profiles as context specifi- cation metrics and a novel relative-importance based attribute selection technique based on N-model. The usefulness of the proposed solution is validated using real-world data sets. Furthermore, the reliability of the proposed risk based resource management model is analysed as a constraint model using linear temporal logic. Based on the experimental results, this research provides meaningful insights to use pragmatic approaches with security and reliability assurances for resource management applications in smart homes

Secure and reliable surveillance over cognitive radio sensor networks in smart grid

In view of recent attacks on smart grid surveillance is of vital importance to enforce surveillance based disaster recovery management operations to ensure seamless energy generation and distribution. The reliability of disaster recovery management depends on availability and privacy preservation of surveillance data. In this paper we propose a reliable privacy preserving smart grid surveillance architecture over cognitive radio sensor networks. Cognitive radio sensor networks are capable of facilitating reliable communications through opportunistic spectrum sensing capabilities as opposed to fixed radio terminal networks based surveillance architectures. The main privacy preserving feature is a novel energy aware physical unclonable function (PUF) based cryptographic key generation method. The proposed solution determines the encryption key length depending on the remaining energy reserve to facilitate data transmission over an expected period of time with minimum channel interferences. Based on the experimental evaluation, the PUF pattern matching based key generation is viable for 32 bits pattern length over a cognitive radio sensor with optimum power utilization and with a probability of reproducibility of a bit pattern (i-p)=0. We have also performed experiments to validate the reliability model using real-world data. In conclusion, our proposed cognitive radio sensor based solution provide more pragmatic insights in reliability assurances for surveillance in smart grid

Reliable delay-sensitive spectrum handoff management for re-entrant secondary users

Opportunistic data transmissions in cognitive radio networks is an effective data transmission mechanism which reduces the limitations of fixed spectrum allocations. In cognitive radio networks secondary users (SUs) can transmit over the available spectrum when the primary users (PUs) are inactive. However, a SU may get interrupted over multiple times when the PU re-appear on that channel for data transmission. For an interrupted SU, efficient spectrum hand-off management is vital to complete the data transmission. Efficient spectrum hand-off necessitates greatly for delay sensitive data transmissions (e.g. time critical remote monitoring applications). For such data transmissions, delay sensitive spectrum hand-off mechanisms are necessary. However, the existing sensed spectrum hand-off management methods do not consider delay bounded repeated attempts for spectrum reallocation when there are multiple interruptions. In this paper a delay sensitive spectrum hand-off management for the re-entrant SUs due to multiple interruptions is proposed. Compared to the existing solutions, the proposed spectrum allocation strategy offers more reliable delay-tolerant opportunities for accessing the spectrum for the re-entrant SUs

Location-dependent disclosure risk based decision support framework for persistent authentication in pervasive computing applications

In pervasive computing applications (e.g. electronic health records), the amount of information permissible to be shared or accessed by mobile users results in high disclosure risks. Obfuscation techniques are desirable in reducing the impact of disclosing confidential information but with a significant loss of utility of information content. Thus, accesses to confi- dential data by mobile users need to be controlled so as to minimize the disclosure risks. To achieve these requirements, we propose a novel location-dependent disclosure risk based decision support framework for persistent authentication and data access management. We have derived the location dependent identity based disclosure risks at record level and file level by using the search theory and entropy. We have experimentally evaluated our proposed model using multi-level security model and fuzzy sets. We have further proved that our proposed technique can significantly reduce the impact of common privacy attacks by performing a comprehensive security analysis. In conclusion, this research presents a novel location-dependent disclosure risk-based decision support framework persistent authentication and a pragmatic data access management approach for highly privacy-sensitive pervasive computing applications

Cloud-based utility service framework for trust negotiations using federated identity management

Utility based cloud services can efficiently provide various supportive services to different service providers. Trust negotiations with federated identity management are vital for preserving privacy in open systems such as distributed collaborative systems. However, due to the large amounts of server based communications involved in trust negotiations scalability issues prove to be less cumbersome when offloaded on to the cloud as a utility service. In this view, we propose trust based federated identity management as a cloud based utility service. The main component of this model is the trust establishment between the cloud service provider and the identity providers. We propose novel trust metrics based on the potential vulnerability to be attacked, the available security enforcements and a novel cost metric based on policy dependencies to rank the cooperativeness of identity providers. Practical use of these trust metrics is demonstrated by analyses using simulated data sets, attack history data: published by MIT Lincoln laboratory, real-life attacks and vulnerabilities extracted from Common Vulnerabilities and Exposures (CVE) repository and fuzzy rule based evaluations. The results of the evaluations imply the significance of the proposed trust model to support cloud based utility services to ensure reliable trust negotiations using federated identity management

Fuzzy logic based load balancing for an online medical consultation system

The aim of a medical consultation system is to deliver reliable healthcare services efficiently. In this paper we propose an online medical consultation system with load balancing as an assurance for maximum resource utilization. Rule-based fuzzy controlled load balancing is applied to a homogeneous system consisting of three medical centers with limited capacities. Performance was comparatively analysed using three weighted rule sets. The results reveal that the load balancing accuracy vary for different rule sets. By incorporating error correction, the accuracy of adjustments is improved with an average of 96%. In conclusion, load balancing is an efficient alternative approach that can avoid possible over-crowding or underloaded situations in an online medical consultation system

Impact of privacy issues on user behavioural acceptance of personalized mHealth services

Health can provide efficient and convenient personalized healthcare services to a variety of patients with diverse medical needs. Existing security vulnerabilities, such as identity theft, loss or theft of mHealth devices and health information raise grave concerns in preserving privacy as well as in promoting user acceptance. With the advent of location dependent personalized mHealth services, access control coupled with cryptographic techniques are seen as pragmatic solutions in preserving privacy of mHealth data in addition to the formal regulatory requirements. In this view, we discuss the impact of privacy threats on user acceptance of personalized mHealth services. As well as the implications of enforcing privacy preserving enforcements at mHealth device level, network level and regulatory measures for sustainable and wide-spread use of personalized mHealth services in future

Security and privacy in cloud computing: Vision, trend and challenges

Cloud computing offers cost-effective solutions via a variety of flexible services. However, security concerns related to managing data, applications, and interactions hamper the rapid deployment of cloud-based services on a large scale. Although many solutions exist, efficiency, scalability, and provable security still have issues that need to be properly addressed. This article explores the various challenges, existing solutions, and limitations of cloud security, with a focus on data utilization management aspects, including data storage, data analytics, and access control. The article concludes with a discussion on future research directions that might lead to more trustworthy cloud security and privacy