Browsing by Author "Baghai-Wadji, Alireza"

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    Detecting network attacks using high-resolution time series
    (2018) Lorgat, Mohamed Wasim; Baghai-Wadji, Alireza
    Research in the detection of cyber-attacks has sky-rocketed in the recent past. However, there remains a striking gap between usage of the proposed algorithms in academic research versus industrial applications. Leading researchers have argued that efforts toward the understanding of proposed detectors are lacking. By digging deeper into their inner workings and critically evaluating their underlying assumptions, better detectors may be built. The aim of this thesis is therefore to provide an underlying theory for understanding a single class of detection algorithms, in particular, anomaly-based network intrusion detection algorithms that utilise high-resolution time series data. A framework is proposed to deconstruct the algorithms into their constituent components (windows, representations, and deviations). The framework is applied to a class of algorithms, allowing to construct a “space” of algorithms spanned by five variables: windowing procedure, information availability, single- or multi-aggregated representation, marginal distribution model, and deviation. The detection of a simple class of Denial-of-Service (DoS) attacks is modelled as a detection theoretic problem. It is shown that the effect of incomplete information is greatest when detecting low-intensity attacks (less than 5%), however, the effect slowly decays as the attack intensity increases. Next, the representation and deviation components are jointly analysed via a proposed experimental procedure using network traffic from two publicly available datasets: the Measurement and Analysis on the WIDE Internet (MAWI) archive, and the Booters dataset. The experimental analysis shows that varying the representation (single- versus multi-aggregated) has little effect on detection accuracy, and that the likelihood deviation is superior to the L2 distance deviation, although the difference is negligible for large-intensity attacks (approximately 80%).
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    Medicine and the Arts Week 4 - Cultivating creative thoughts
    (2015-01-21) Baghai-Wadji, Alireza
    In this video, Alireza Baghai-Wadji, and electrical engineer, explores various questions related to creativity, its origins, and the idea that creativity is an ability that can be acquired. He also he discusses his ideas about the converging worlds of physics, philosophy, and neuroscience in order to get us thinking about the processes that drive our most inventive thinking. This is the second video in Week 4 of the Medicine and the Arts Massive Open Online Course.
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    Medicine and the Arts Week 4 - In dialogue about creativity
    (2015-01-21) Reid, Steve; Dyer, Silke; Bonnici, Francois; Baghai-Wadji, Alireza
    In this video, Steve Reid poses additional questions to Alireza Baghai-Wadji, François Bonnici, and Silke Dyer around the topic of creativity. Silke taslks about new developments in reproductive health, François comments on how to create new ways of thinking to tackle complex problems, and Alireza discusses how the biological conception in humans is analogus to the creation of new ideas. This is the fifthvideo in Week 4 of the Medicine and the Arts Massive Open Online Course.
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    Modelling computer network traffic using wavelets and time series analysis
    (2019) Ntlangu, Mbulelo Brenwen; Baghai-Wadji, Alireza
    Modelling of network traffic is a notoriously difficult problem. This is primarily due to the ever-increasing complexity of network traffic and the different ways in which a network may be excited by user activity. The ongoing development of new network applications, protocols, and usage profiles further necessitate the need for models which are able to adapt to the specific networks in which they are deployed. These considerations have in large part driven the evolution of statistical profiles of network traffic from simple Poisson processes to non-Gaussian models that incorporate traffic burstiness, non-stationarity, self-similarity, long-range dependence (LRD) and multi-fractality. The need for ever more sophisticated network traffic models has led to the specification of a myriad of traffic models since. Many of these are listed in [91, 14]. In networks comprised of IoT devices much of the traffic is generated by devices which function autonomously and in a more deterministic fashion. Thus in this dissertation the activity of building time series models for IoT network traffic is undertaken. In the work that follows a broad review of the historical development of network traffic modelling is presented tracing a path that leads to the use of time series analysis for the said task. An introduction to time series analysis is provided in order to facilitate the theoretical discussion regarding the feasibility and suitability of time series analysis techniques for modelling network traffic. The theory is then followed by a summary of the techniques and methodology that might be followed to detect, remove and/or model the typical characteristics associated with network traffic such as linear trends, cyclic trends, periodicity, fractality, and long range dependence. A set of experiments is conducted in order determine the effect of fractality on the estimation of AR and MA components of a time series model. A comparison of various Hurst estimation techniques is also performed on synthetically generated data. The wavelet-based Abry-Veitch Hurst estimator is found to perform consistly well with respect to its competitors, and the subsequent removal of fractality via fractional differencing is found to provide a substantial improvement on the estimation of time series model parameters.
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    Quantum-Based Modelling and Simulation of Materials Used In Small-Scaled Plasmonic Devices
    (2021) Akinyemi, Lateef Adesola; Baghai-Wadji, Alireza
    The study of Plasmonics has been evolutionary and fascinating over the last few decades. This area of research has attracted extensive interest predominantly as a result of the possibility to direct and confine light at the nano-scale level using metallic materials as fundamental building blocks. This provides an explanation to the interaction of light and nano-sized metallic devices. The optical properties of nano-structured systems depend on the collective resonance of conducting electrons which are determined by the geometrical features as well the polarization characteristics of the incident light and frequency. For large systems with size in the order of tens of the size of nano-scale materials, the response has been extensively studied and is now well understood. The response of these large systems can be described using classical Maxwell's equations with reasonable accuracy. On the other hand, the application of this classical solution to Plasmonic-based nano-particles is severely limited by quantum phenomena such as tunnelling and non-local screening. These quantum effects can neither be described nor explained by the classical method. There is, therefore, the need to understand how the theory of quantum method can be utilized to describe the properties of nano-materials. This forms the main focus of this thesis. Firstly, the extension of existing classical models to Plasmonic materials is examined. It is ascertained that most of these models are designed for usage only in the highfrequency region. Furthermore, the use of Plasmonic devices including Metal-InsulatorMetal (MIM) and Insulator-Metal-Insulator (IMI) configuration are explored. An algorithm to determine and generate imaginary wave-vector numerically is proposed. The dissipation of electromagnetic fields, dielectric function, electric field, and magnetic field are computed for various values of complex wave-vector. Additionally, a one-dimensional quantum-based frequency-dependent dielectric function for small-scale devices is investigated. A Rigorous analysis and approximation are carried out on a 1-D model and a nano-wire geometry. The effect of transition band and investigation of optical materials of nano-wire for 1-D, 2-D, and 3-D are included in the analysis. The Eigen-pairs of the underlying canonical and associated perturbed quantum systems are computed and utilized for this study. Galerkin's method has been employed to discretize the boundary-value of interest. The introduction of the Sinc function throughout the analysis ensures the robustness and soundness of the computation. The analytical and numerical results demonstrate that the real- and imaginary parts of the dielectric function are even and odd function, respectively, as expected. More so, the cubical, cuboid and spherical geometries of metallic nano-particles are employed to examine the effects on the material. These effects of size-dependent damping constant on the metallic nano-particles and not forgetting the optical properties of the material such as dielectric function, refractive index and absorption coefficient which are considered worthy of investigation in this thesis. Hence, in this thesis, a customized, flexible, and computational software package for efficient assessment of small-scaled Plasmonic devices has been developed in 1-D and 2-D for regular geometry using the Standard Finite Difference Method (SFDM) and Conservative Finite Difference Method (CFDM). This study has been motivated, predominantly, by the tremendous interest in the examination of these geometrical structures. The package enables us to solve the desired homogeneous Dirichlet boundary-value problem. Then, the developed program is applied to solve the time-independent Schr¨odinger equation for the modelling and simulation of the physical properties of metallic nano-particles. Furthermore, the results obtained from SFDM and CFDM are compared. Finally, the concept of density functional theory (DFT) alongside the Kohn-Sham equation boundary-value problem with Dirichlet condition has been employed and demonstrated in this thesis to be a good candidate in solving and computing optical properties such as frequency-dependent dielectric function in small scale metallic nanoparticle of interest. Additionally, neither CFDM nor DFT schemes have been applied to determine optical properties of metallic nanoparticles in the literature, making it the first time such schemes will be used to compute optical properties.
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    Reconstruction of Functions From Non-uniformly Distributed Sampled Data in Shift-Invariant Frame Subspaces
    (2018) Mkhaliphi, Mkhuseli Bruce; Baghai-Wadji, Alireza
    The focus of this research is to study and implement efficient iterative reconstruction algorithms. Iterative reconstruction algorithms are used to reconstruct bandlimited signals in shift-invariant L2 subspaces from a set of non-uniformly distributed sampled data. The Shannon-Whittaker reconstruction formula commonly used in uniform sampling problems is insufficient in reconstructing function from non-uniformly distributed sampled data. Therefore new techniques are required. There are many traditional approaches for non-uniform sampling and reconstruction methods where the Adaptive Weights (AW) algorithm is considered to be the most efficient. Recently, the Partitions of Unity (PoU) algorithm has been suggested to outperform the AW although there has been much literature covering its numerical performance. A study and analysis of the implementation of the Adaptive Weights (AW) and Partitions of Unity (PoU) reconstruction methods is conducted. The algorithms consider the missing data problem, defined as reconstructing continuous-time (CT) signals from non-uniform samples which resulted from missing samples on a uniform grid. Mainly, the algorithms convert the non-uniform grid to a uniform grid. The implemented iterative methods construct CT bandlimited functions in frame subspaces. Bandlimited functions are considered to be a superposition of basis functions, named frames. PoU is a variation of AW, they differ by the choice of frame because each frame produces a different approximation operator and convergence rate. If efficiency is defined as the norm convergence and computational time of an algorithm, then among the two methods, discussed, the PoU method is more efficient. The AW method is slow and converged to a higher error than that of the PoU. However, AW compensates for its slowness and less accuracy by being convergent and robust for large sampling gaps and less sensitive to the sampling irregularities. The impact of additive white Gaussian noise on the performance of the two algorithms is also investigated. The numerical tools utilized in this research consist of the theory of discrete irregular sampling, frames, and iterative techniques. The developed software provides a platform for sampling signals under non-ideal conditions with real devices.
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    Uniaxial Strain Effect on Graphene-Nanoribbon Resonant Tunneling Transistors
    (2018) Akbari, Mahmood; Baghai-Wadji, Alireza
    Graphene is an atomically thin two-dimensional (2-D) crystal with unique thermal, mechanical, and electronic transport properties such as the high mobility of carriers, perfect 2- D confinement and linear dispersion, etc., has been attracted many interest as a promising candidate for nano-scale devices over the past decades. Multilayer stacks of graphene and other stable, atomically thin, 2-D materials offer the prospect of creating a new class of heterostructure materials. Hexagonal boron- nitride (hBN), is a great candidate to be stacked with graphene due to an atomically 2-D layered structure with a lattice constant very similar to graphene (1.8% mismatch), large electrical band gap (∼4.7eV), and excellent thermal and chemical stability. The graphene/hBN based tunneling transistors show the resonant tunneling and strong negative differential resistance (NDR). These devices which have potential for future high-frequency and logic applications such as high-speed IC circuits, signal generators, data storage, etc., has been studied both theoretically and experimentally recently. The aim in this dissertation has been to study the effect of the uniaxial strain on the graphene nanoribbon resonant tunneling transistors (RTTs). The uniaxial strain may be induced either by an external stress applied to the graphene in a particular direction or by a substrate due to deposition of graphene on top of the other materials. The strain modifies distances between carbon atoms which leading to different hopping amplitudes among neighboring sites. A resonant tunneling transistor consisting of armchair graphene nanoribbon (AGNR) electrodes with three layers of hBN tunnel barrier between them has been considered. By using the nearest-neighbor tight-bind (TB) method and the nonequilibrium Green function (NEGF) formalism, the electronic transport characteristics of a RTT is calculated. In this work, we focus on how the strain affects the current-voltage characteristics of AGNR/hBN RTT.