Sentiment analysis as a service

This research focuses on the design and development of a service composition based framework that enables the execution of services for social media based sentiment analysis. Our research develops novel analytical models, composition techniques and algorithms which use services as a mean for sentiment abstraction, processing and analysis from large scale social media data. Current sentiment analysis techniques require specialized skill of data science and machine learning. Moreover, traditional approaches rely on laborious and time-consuming activities such as manual dataset labelling, data model training and validation. This makes overall sentiment analysis process a challenging task. In comparison, services are `ready-made' software solutions that can be composed on-demand for developing complex applications without indulging in the domain specific details. This thesis investigates a novel approach that transforms traditional social media based sentiment analysis process into a service composition driven solution. In this thesis, we begin by developing a novel service framework that replaces the traditional sentiment analysis tasks with online services. Our framework includes a new service model to present services required for sentiment analysis. We develop a semantic service composition model and algorithm that dynamically composes various services for data collection, noise filtering and sentiment extraction. In particular, we focus on abstracting sentiment based on location and time. We then focus on enhancing the flexibility of our proposed service framework to compose appropriate sentiment analysis services for highly dynamic and changing features of social media platforms. In addition, we aim to efficiently process and analyze large scale social media data. In order to enhance our service composition framework, we propose a novel approach to formalize social media platforms as cloud enabled services. We develop a functional and quality of service (QoS) model that captures various dynamic features of social media platforms. In addition, we devise a cloud based service model to access social media platforms as services by using the Ontology Web Language for Service (OWL-S). Secondly, we integrate the QoS model into our existing composition framework. It enables our framework to dynamically assess the QoS of multiple social media platforms, and simultaneously compose appropriate services to extract, process, analyze and integrate the sentiment results from large scale data. Finally, we concentrate on efficient utilization of the sentiment analysis extracted from large scale data. We formulate a meta-information composition model that transforms and stores sentiment obtained from large streams of data into re-usable information. Later, the re-usable information is on-demand integrated and delivered to end users. To demonstrate the performance and test the effectiveness of our proposed models, we develop prototypes to evaluate our composition framework. We design several scenarios and conduct a series of experiments using real-world social media datasets. We present the results and discuss the outcomes which validate the performance of our research

Discovering and Utilising Expert Knowledge from Security Event Logs

Security assessment and configuration is a methodology of protecting computer systems from malicious entities. It is a continuous process and heavily dependent on human experts, which are widely attributed to being in short supply. This can result in a system being left insecure because of the lack of easily accessible experience and specialist resources. While performing security tasks, human experts often revert to a system's event logs to determine status of security, such as failures, configuration modifications, system operations etc. However, finding and exploiting knowledge from event logs is a challenging and time-consuming task for non-experts. Hence, there is a strong need to provide mechanisms to make the process easier for security experts, as well as providing tools for those with significantly less security expertise. Doing so automatically allows for persistent and methodical testing without an excessive amount of manual time and effort, and makes computer security more accessible to on-experts. In this thesis, we present a novel technique to process security event logs of a system that have been evaluated and configured by a security expert, extract key domain knowledge indicative of human decision making, and automatically apply acquired knowledge to previously unseen systems by non-experts to recommend security improvements. The proposed solution utilises association and causal rule mining techniques to automatically discover relationships in the event log entries. The relationships are in the form of cause and effect rules that define security-related patterns. These rules and other relevant information are encoded into a PDDL-based domain action model. The domain model and problem instance generated from any vulnerable system can then be used to produce a plan-of-action by employing a state-of-the-art automated planning algorithm. The plan can be exploited by non-professionals to identify the security issues and make improvements. Empirical analysis is subsequently performed on 21 live, real world event log datasets, where the acquired domain model and identified plans are closely examined. The solution's accuracy lies between 73% - 92% and gained a significant performance boost as compared to the manual approach of identifying event relationships. The research presented in this thesis is an automation of extracting knowledge from event data steams. The previous research and current industry practices suggest that this knowledge elicitation is performed by human experts. As evident from the empirical analysis, we present a promising line of work that has the capacity to be utilised in commercial settings. This would reduce (or even eliminate) the dire and immediate need for human resources along with contributing towards financial savings