Philosophiae Doctor - PhD (Computer Science)
Permanent URI for this collectionhttps://hdl.handle.net/10566/14796
Browse
Recent Submissions
Item type: Item , Energy optimization for wireless sensor networks using hierarchical routing techniques(University of the Western Cape, 2015) Abidoye, Ademola Philip; Bagula, Antoine B.Wireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the research and the practitioner communities due to their wide area of applications. These applications include real-time sensing for audio delivery, imaging, video streaming, and remote monitoring with positive impact in many fields such as precision agriculture, ubiquitous healthcare, environment protection, smart cities and many other fields. While WSNs are aimed to constantly handle more intricate functions such as intelligent computation, automatic transmissions, and in-network processing, such capabilities are constrained by their limited processing capability and memory footprint as well as the need for the sensor batteries to be cautiously consumed in order to extend their lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by proposing a novel clustering approach for routing the sensor readings in wireless sensor networks. The main contribution of this dissertation is to 1) propose corrective measures to the traditional energy model adopted in current sensor networks simulations that erroneously discount both the role played by each node, the sensor node capability and fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at maximizing sensor networks lifetime. We propose three energy models for sensor network: a) a service-aware model that account for the specific role played by each node in a sensor network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint.Item type: Item , The importance of time: modelling network intrusions with long short-term memory recurrent neural networks(University of the Western Cape, 2024) Staudemeyer Ralf Colmar; Omlin Christian WWe claim that modelling network traffic, as a time series with a supervised learning approach. using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM) recurrent neural networks with the training data provided by the DARPA KDD Cup 99 challenge. After preprocessing all features. to improve information gain, we applied a number of intuitive steps to extract salient features, which resulted in the creation of a number of minimal feature sets that could be used for detecting attack classes. The preprocessed KDD Cup 99 data was then used to test the perfomance of five very common and well-known classifiers: Decision trees. naive Bayes. Bayesian networks. feedforward natural network, and support vector machines. Our results show a performance comparable to the winning entries of the KDD Cup 99 challenge. Finally. we applied the LSTM recurrent neural network classifier to the preprocessed data using the minimal feature sets. Our results show that the LSTM classifier provides superior performance in compc1 rison to other strong static classifiers trained. This is due to the fact that, LSTM network learns to look back in time and correlate consecutive collection records. For the first time ever we have demonstrated the usefulness of LSTM networks to intrusion detection.Item type: Item , The importance of time: modelling network intrusions with long short-term memory recurrent neural networks(University of the Western Cape, 2024) Staudemeyer, Ralf Colmar; Omlin, Christian WWe claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM) recurrent neural networks with the training data provided by the DARPA KDD Cup 99 challenge. After preprocessing all frature, to improve information gain. we applied a number of intuitive steps to extract salient features,which resulted in the creation of a number of minimal fcature sets that could be used for detecting attack classes. The preprocessed KDD Cup 99 data was then used to test the performance of five very common and well-known classifiers. Decision trees. naive Bayes, Bayesian networks, feed forward neural network and support vector machines. Our results show a performance comparable to the winning entries of the KDD Cup 99 challengc. Finally, we applied the LSTM recurrent neural network classifier to the preprocessed data using the minimal feature sets. Our results show that the LSTM classifier provides superior performance in comparison to other strong static classifiers t rained. This is due to the fact that, LSTM learns to look back in time and correlate constructive collection records. For the' first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.Item type: Item , Towards co-production of technology artefacts with a disabled community: A case study with deaf people and protection of personal information with video relay services(University of the Western Cape, 2024) Henney, André John; Tucker, William D.This thesis offers evidence of the realisation and use of this new methodological approach to move towards the genuine co-production of technology artefacts when working with marginalised Deaf people, particularly concerning their privacy needs surrounding a video relay service. Deaf people throughout the world, including those in South Africa, experience difficulty in essential communication in an environment that mainly comprises hearing individuals. This communication divide exists because Deaf, as opposed to deaf, people communicate in sign language; which most of the population does not comprehend. South Africa currently provides limited video relay services; the Protection of Personal Information Act was recently introduced, and South African Sign Language became the 12th official language. Thus, we ask: What are the major considerations for moving from co-design to co-production of technological artefacts with a case study of Deaf people's protection of personal information for video relay services? The move toward a co-produced workshop on the protection of personal information in South African Sign Language began with Video Relay Service (VRS) as the initial use case and the positive deviants in the Deaf community driving the process forward. By adopting a pragmatist theory to guide an Agile UX (software development) approach to co-design, combined with a Participatory Action Research approach, space was made for a collaborative process to emerge.