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(2022)ThesisDespite the availability of toolbars and studies in phishing, the number of phishing attacks has been increasing in the past years. It remains a challenge to develop robust phishing detection systems due to the continuous change of attack models. We attempt to address this by designing an adaptive phishing detection system with the ability to continually learn and detect phishing robustly. In the first work, we demonstrate a systematic way to develop a novel phishing detection approach using compression algorithm. We also propose the use of compression ratio as a novel machine learning feature, which significantly improves machine learning based phishing detection over previous studies. Our proposed method outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80.04%. In the following work, we propose a feature-free method using Normalised Compression Distance (NCD), a metric which computes the similarity of two websites by compressing them, eliminating the need to perform any feature extraction. This method examines the HTML of webpages and computes their similarity with known phishing websites. Our approach is feasible to deploy in real systems with a processing time of roughly 0.3 seconds, and significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a G-mean score of 94.47%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. We also discuss the implication of automation offered by AutoML frameworks towards the role of human experts and data scientists in the domain of phishing detection. Our work investigates whether models that are built using AutoML frameworks can outperform the results achieved by human data scientists in phishing datasets and analyses the relationship between the performances and various data complexity measures. There remain many challenges for building a real-world phishing detection system using AutoML frameworks due to the current support only for supervised classification problems, leading to the need for labelled data, and the inability to update the AutoML-based models incrementally. This indicates that experts with knowledge in the domain of phishing and cybersecurity are still essential in phishing detection.
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(2021)ThesisMultiferroic BiFe0.5Cr0.5O3 (BFCO) in which ferroelectric and magnetic orders coexist has gained research interest owing to its potential applications, e.g., spintronic and resistive random-access memory. Moreover, multiferroics possess a narrower bandgap compared to typical ferroelectrics, extending their application to photovoltaic devices. In contrast to the conventional semiconductors, the polarization-induced electric field facilitates the photoexcited charge separation, leading to an above-bandgap photovoltage in ferroelectrics. Nevertheless, a long-standing issue is the relatively low absorption of visible light. Thus, it is essential but challenging to tune their bandgap without compromising ferroelectricity. This thesis explores structural phase transition in the epitaxial BFCO films grown on SrRuO3 buffered (001) SrTiO3 substrate via Laser Molecular Beam Epitaxy (LMBE). Reciprocal space mapping result shows strain relaxation mechanism is not solely by the formation of misfit dislocation but also by changing the crystal symmetry, transitioning from tetragonal-like to a monoclinically distorted phase as the thickness increases. The crystallographic evolution is also coupled with bandgap modulation, confirming that BFCO structure and its physical properties are strongly intertwined. Using spectroscopic ellipsometry, the slight redshift of the bandgap distinguishes the absorption process of the T-like BFCO layer from that of monoclinically distorted structure, further confirmed by spectral photocurrent measurement via conductive-atomic force microscopy. The preparation of pure phase BFCO film with a robust polarization is of paramount importance for practical application. Yet, similar to the parental bismuth ferrite, BFCO suffers from poor electrical leakage performance. We report a three-order of magnitude suppression in the leakage current for the BFCO film through judicious adjustment of the growth rate. Scanning probe microscopy (PFM, AFM and c-AFM) results reveal that both microstructure and ferroelectric properties can be tuned by lowering the growth rate, ensuing realization of the room-temperature ferroelectric polarization comparable to the ab-initio predicted value. This thesis provides a facile strategy to tailor the structure-property of epitaxial BFCO film and its functional response for emerging optoelectronic devices.