A cyber exercise post assessment framework: In Malaysia perspectives

Critical infrastructures are based on complex systems that provide vital services to the nation. The complexities of the interconnected networks, each managed by individual organisations, if not properly secured, could offer vulnerabilities that threaten other organisations’ systems that depend on their services. This thesis argues that the awareness of interdependencies among critical sectors needs to be increased. Managing and securing critical infrastructure is not isolated responsibility of a government or an individual organisation. There is a need for a strong collaboration among critical service providers of public and private organisations in protecting critical information infrastructure. Cyber exercises have been incorporated in national cyber security strategies as part of critical information infrastructure protection. However, organising a cyber exercise involved multi sectors is challenging due to the diversity of participants’ background, working environments and incidents response policies. How well the lessons learned from the cyber exercise and how it can be transferred to the participating organisations is still a looming question. In order to understand the implications of cyber exercises on what participants have learnt and how it benefits participants’ organisation, a Cyber Exercise Post Assessment (CEPA) framework was proposed in this research. The CEPA framework consists of two parts. The first part aims to investigate the lessons learnt by participants from a cyber exercise using the four levels of the Kirkpatrick Training Model to identify their perceptions on reaction, learning, behaviour and results of the exercise. The second part investigates the Organisation Cyber Resilience (OCR) of participating sectors. The framework was used to study the impact of the cyber exercise called X Maya in Malaysia. Data collected through interviews with X Maya 5 participants were coded and categorised based on four levels according to the Kirkpatrick Training Model, while online surveys distributed to ten Critical National Information Infrastructure (CNII) sectors participated in the exercise. The survey used the C-Suite Executive Checklist developed by World Economic Forum in 2012. To ensure the suitability of the tool used to investigate the OCR, a reliability test conducted on the survey items showed high internal consistency results. Finally, individual OCR scores were used to develop the OCR Maturity Model to provide the organisation cyber resilience perspectives of the ten CNII sectors.

Transmission electron tomography: quality assessment and enhancement for three-dimensional imaging of nanostructures

Nanotechnology has revolutionised humanity's capability in building microscopic systems by manipulating materials on a molecular and atomic scale. Nan-osystems are becoming increasingly smaller and more complex from the chemical perspective which increases the demand for microscopic characterisation techniques. Among others, transmission electron microscopy (TEM) is an indispensable tool that is increasingly used to study the structures of nanosystems down to the molecular and atomic scale. However, despite the effectivity of this tool, it can only provide 2-dimensional projection (shadow) images of the 3D structure, leaving the 3-dimensional information hidden which can lead to incomplete or erroneous characterization. One very promising inspection method is Electron Tomography (ET), which is rapidly becoming an important tool to explore the 3D nano-world. ET provides (sub-)nanometer resolution in all three dimensions of the sample under investigation. However, the fidelity of the ET tomogram that is achieved by current ET reconstruction procedures remains a major challenge. This thesis addresses the assessment and advancement of electron tomographic methods to enable high-fidelity three-dimensional investigations. A quality assessment investigation was conducted to provide a quality quantitative analysis of the main established ET reconstruction algorithms and to study the influence of the experimental conditions on the quality of the reconstructed ET tomogram. Regular shaped nanoparticles were used as a ground-truth for this study. It is concluded that the fidelity of the post-reconstruction quantitative analysis and segmentation is limited, mainly by the fidelity of the reconstructed ET tomogram. This motivates the development of an improved tomographic reconstruction process. In this thesis, a novel ET method was proposed, named dictionary learning electron tomography (DLET). DLET is based on the recent mathematical theorem of compressed sensing (CS) which employs the sparsity of ET tomograms to enable accurate reconstruction from undersampled (S)TEM tilt series. DLET learns the sparsifying transform (dictionary) in an adaptive way and reconstructs the tomogram simultaneously from highly undersampled tilt series. In this method, the sparsity is applied on overlapping image patches favouring local structures. Furthermore, the dictionary is adapted to the specific tomogram instance, thereby favouring better sparsity and consequently higher quality reconstructions. The reconstruction algorithm is based on an alternating procedure that learns the sparsifying dictionary and employs it to remove artifacts and noise in one step, and then restores the tomogram data in the other step. Simulation and real ET experiments of several morphologies are performed with a variety of setups. Reconstruction results validate its efficiency in both noiseless and noisy cases and show that it yields an improved reconstruction quality with fast convergence. The proposed method enables the recovery of high-fidelity information without the need to worry about what sparsifying transform to select or whether the images used strictly follow the pre-conditions of a certain transform (e.g. strictly piecewise constant for Total Variation minimisation). This can also avoid artifacts that can be introduced by specific sparsifying transforms (e.g. the staircase artifacts the may result when using Total Variation minimisation). Moreover, this thesis shows how reliable elementally sensitive tomography using EELS is possible with the aid of both appropriate use of Dual electron energy loss spectroscopy (DualEELS) and the DLET compressed sensing algorithm to make the best use of the limited data volume and signal to noise inherent in core-loss electron energy loss spectroscopy (EELS) from nanoparticles of an industrially important material. Taken together, the results presented in this thesis demonstrates how high-fidelity ET reconstructions can be achieved using a compressed sensing approach.