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Full-Text Articles in Engineering
Novel Estimation And Detection Techniques For 5g Networks, Anas Saci
Novel Estimation And Detection Techniques For 5g Networks, Anas Saci
Electronic Thesis and Dissertation Repository
The thesis presents several detection and estimation techniques that can be incorporated into the fifth-generation (5G) networks. First, the thesis presents a novel system for orthogonal frequency division multiplexing (OFDM) to estimate the channel blindly. The system is based on modulating particular pairs of subcarriers using amplitude shift keying (ASK) and phase-shift keying (PSK) adjacent in the frequency domain, which enables the realization of a decision-directed (DD) one-shot blind channel estimator (OSBCE). The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo …
Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo
Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo
Electronic Thesis and Dissertation Repository
The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …
Collective Contextual Anomaly Detection For Building Energy Consumption, Daniel Berhane Araya
Collective Contextual Anomaly Detection For Building Energy Consumption, Daniel Berhane Araya
Electronic Thesis and Dissertation Repository
Commercial and residential buildings are responsible for a substantial portion of total global energy consumption and as a result make a significant contribution to global carbon emissions. Hence, energy-saving goals that target buildings can have a major impact in reducing environmental damage. During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes the \textit{ensemble anomaly detection} (EAD) framework. The EAD is …
Contextual Anomaly Detection Framework For Big Sensor Data, Michael Hayes
Contextual Anomaly Detection Framework For Big Sensor Data, Michael Hayes
Electronic Thesis and Dissertation Repository
Performing predictive modelling, such as anomaly detection, in Big Data is a difficult task. This problem is compounded as more and more sources of Big Data are generated from environmental sensors, logging applications, and the Internet of Things. Further, most current techniques for anomaly detection only consider the content of the data source, i.e. the data itself, without concern for the context of the data. As data becomes more complex it is increasingly important to bias anomaly detection techniques for the context, whether it is spatial, temporal, or semantic. The work proposed in this thesis outlines a contextual anomaly detection …