Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Aggregating privatized medical data for secure querying applications

 This thesis analyses and examines the challenges of aggregation of sensitive data and data querying on aggregated data at cloud server. This thesis also delineates applications of aggregation of sensitive medical data in several application scenarios, and tests privatization techniques to assist in improving the strength of privacy and utility

Cloud-based homomorphic encryption for privacy-preserving machine learning in clinical decision support

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that ensures secure processing of sensitive data via untrusted networks in the public cloud or by third-party cloud vendors. It relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data meaningfully while still in encrypted form; although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. Machine Learning (ML) is employed in CDS applications that typically learn and can personalise actions based on individual behaviour. A relatively simple-to-implement, common and consistent framework is sought that can overcome most limitations of Fully Homomorphic Encryption (FHE) in order to offer an expanded and flexible set of HE capabilities. In the absence of a significant breakthrough in FHE efficiency and practical use, it would appear that a solution relying on client interactions is the best known entity for meeting the requirements of private CDS-based computation, so long as security is not significantly compromised. A hybrid solution is introduced, that intersperses limited two-party interactions amongst the main homomorphic computations, allowing exchange of both numerical and logical cryptographic contexts in addition to resolving other major FHE limitations. Interactions involve the use of client-based ciphertext decryptions blinded by data obfuscation techniques, to maintain privacy. This thesis explores the middle ground whereby HE schemes can provide improved and efficient arbitrary computational functionality over a significantly reduced two-party network interaction model involving data obfuscation techniques. This compromise allows for the powerful capabilities of HE to be leveraged, providing a more uniform, flexible and general approach to privacy-preserving system integration, which is suitable for cloud deployment. The proposed platform is uniquely designed to make HE more practical for mainstream clinical application use, equipped with a rich set of capabilities and potentially very complex depth of HE operations. Such a solution would be suitable for the long-term privacy preserving-processing requirements of a cloud-based CDS system, which would typically require complex combinatorial logic, workflow and ML capabilities

Factors Influencing Cloud Computing Adoption by Small Firms in the Payment Card Industry

Technology acceptance is increasingly gaining attention in research considering the continuous exploits of innovation and various derived advantages. Cloud computing (CC) has shown to be the ideal solution for aligning information technology with business strategies. However, small to medium-sized enterprises (SMEs) in the payment card industry are reluctantly adopting this technology despite the benefits. This correlational study aims at investigating whether security, cost effectiveness, or regulatory compliance influence CC adoption by U.S. SMEs in the payment card sector. The study builds on the technology-organization-environment (TOE) framework and uses a previously validated instrument to assess CC adoption by decision-makers in U.S. SMEs handling payment data. A multiple linear regression analysis of survey data from 140 participants indicated that the model could predict CC acceptance. Cost effectiveness and regulatory compliance significantly predicted the decision to adopt CC with a strong and positive effect. Pearson’s coefficients indicated a significant correlation between each independent variable and the outcome variable. Leaders in small payment markets may gain the latest insights on cloud services in their technology decisions. Cloud service providers may be well informed on consumers’ demands for the effective delivery of products and services. Implications for positive social change include enhanced cloud security to reduce compliance defects, cybersecurity attacks, and small business failures. This study may increase consumers’ confidence and comfort while using their credit or debit cards in various sales outlets, thus boosting business performance and employment with a better quality of life

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

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Embedding risk management within new product and service development of an innovation and risk management framework and supporting risk processes, for effective risk mitigation : an action research study within the Information and Communication Technology (ICT) Sector

At first glance, innovation and risk management seem like two opposing disciplines with diverse objectives. The former seeks to be flexible and encourages enhanced solutions and new ideas, while the latter can be seen as stifling such innovative thinking. Since there is a failure rate of as many as eight out of every ten products launched, it is perhaps necessary for organisations to consider applying more structured approaches to innovation, in order to better manage risks and to increase the chances of delivering improved goods and services. A risk management approach is well suited to address the challenge of failure, as it focuses not only on the negative impact of risks but also on the opportunities they present. It aligns these with the strategic objectives of the organisation to increase the chances of its success. The research objective of this study was to establish how to embed risk management within the innovation divisions of an organisation to ensure that more efficient products and services are delivered to customers. To achieve this end, action research was conducted in a large organisation operating in a high-technology environment that launches many diverse products and services and rapidly expanding service offerings to other industries. The study took four years to complete and delivered multiple interventions that successfully embedded risk management within the organisation, leading to changed behaviours and double-loop learning. Two main knowledge contributions are offered by the study. Firstly, a generic and empirically validated integrated Innovation and Risk Management Framework (IRMF) is developed and guides new product and service development by considering both best practices and risks. Secondly, a risk dashboard is designed as a design science artefact within the action research cycles, which consolidates all the knowledge that was generated during the study. This is ultimately a visual interface to support stage-gate decision making. Since the context of the study was broad, extensive and complicated, the use of mixed-method research complemented and expanded on the findings by providing another layer of support and validation. This thesis highlights the complexity of innovation and presents the need for an organising framework that will encourage innovation but is sufficiently flexible to cater for diverse needs and risks. The study delivers several other, valuable contributions regarding what, how and why incidents occur within the real-world context of new product and service development. Several generic artefacts, such as risk processes and maturity frameworks, are also developed, which can guide risk and new product and service development practitioners to deliver more efficient product and services. This study offers several novel approaches to evaluating risks and provides practical support and recommendations, addressing shortcomings of fragmented research in similar, but smaller-scale studies that have been conducted in information systems. It is the premise of this research that a much wider number of risks need to be managed as new products and services are developed, than was noted in previous studies. Effective risk management in new product and service development could lead to competitive advantage for organisations by increasing knowledge and facilitating sustainable, informed risk decision-making

Evaluating the Impact of Security Measures on Performance of Secure Web Applications Hosted on Virtualised Platforms

The use of web applications has drastically increased over the years, and so has the need to secure these applications with effective security measures to ensure security and regulatory compliance. The problem arises when the impact and overheads associated with these security measures are not adequately quantified and factored into the design process of these applications. Organizations often resort to trading-off security compliance in order to achieve the required system performance. The aim of this research work is to quantify the impact of security measures on system performance of web applications and improve design decision-making in web application design process. This research work examines the implications of compliance and security measures on web applications and explores the possibility of extending the existing Queueing Network (QN) based models to predict the performance impact of security on web applications. The intention is that the results of this research work will assist system and web application designers in specifying adequate system capacity for secure web applications, hence ensuring acceptable system performance and security compliance. This research work comprises three quantitative studies organized in a sequential flow. The first study is an exploratory survey designed to understand the extent and importance of the security measures on system performance in organizations. The survey data was analyzed using descriptive statistics and Factor Analysis. The second study is an experimental study with a focus on causation. The study provided empirical data through sets of experiments proving the implications of security measures on a multi-tiered state-of-the-art web application - Microsoft SharePoint 2013. The experimental data were analyzed using the ANCOVA model. The third study is essentially a modeling-based study aimed at using the insights on the security implications provided by the second study. In the third study, using a well-established QN result - Mean Value Analysis (MVA) for closed networks, the study demonstrated how security measures could be incorporated into a QN model in an elegant manner with limited calculations. The results in this thesis indicated significant impact of security measures on web application with respect to response time, disk queue length, SQL latches and SQL database wait times. In a secure three-tiered web application the results indicated greater impacts on the web tier and database tier primarily due to encryption requirements dictated by several compliance standards, with smaller impact seen at the application tier. The modeling component of this thesis indicated a potential benefit in extending QN models to predict secure web application performance, although more work is needed to enhance the accuracy of the model. Overall, this research work contributes to professional practice by providing performance evaluation and predictive techniques for secure web applications that could be used in system design. From performance evaluations and QN modeling perspective, although three-tiered web application modeling has been widely studied, the view in this thesis is that this is the first attempt to look at security compliance in a three-tiered web application modeling on virtualized platforms