Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers

Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods

Handwritten Digit Recognition and Classification Using Machine Learning

In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy

Acceleration of a Dynamically Packed Oblique Sparse Projection Random Forest

The proliferation of scientific and industrial sensors is causing an accelerating deluge of data, the processing of which into actionable knowledge requires fast and accurate machine learning methods. A class of algorithms suited to process these large amounts of data is decision forests, widely used methods known for their versatility, state of the art inference, and fast model training. Oblique Sparse Projection Forests — OSPFs — are a subset of decision forests, which provide data inference superior to other methods. Despite providing state of the art inference and having a computational complexity similar to other popular decision forests, there are no OSPF implementations that scale beyond trivially sized datasets. We explore whether OSPF training and inference speeds can compete with other popular decision forest variants despite an algorithmic incompatibility which prevent OSPFs from using traditional forest training optimizations. First, using R, we implement a highly extensible proof of concept version of a recently conceived OSPF, Randomer Forest, shown to provide state of the art results on many datasets and provide this system for general use via CRAN. We then develop and implement a postprocessing method, Forest Packing, to pack the nodes of a trained forest into a novel data structure and modify the ensemble traversal method to accelerate forest based inferences. Finally, we develop FastRerF, an optimized version of Randomer Forest which dynamically performs forest packing during training. The initial implementation in R provided training speeds inline with other decision forest systems and scaled better with additional resources, but used an excessive amount of memory and provided slow inference speeds. The development of Forest Packing increased inference throughput by almost an order of magnitude as compared to other systems while greatly reducing prediction latency. FastRerF model training is faster than other popular decision forest systems when using similar parameters and trains Random Forests faster than the current state of the art. Overall, we provide data scientists a novel OSPF system with R and Python front ends, which trains and predicts faster than other decision forest implementations

Machine learning classification of microbial community compositions to predict anthropogenic pollutants in the Baltic Sea

Microbial communities react rapidly and specifically to changing environments, indicating distinct microbial fingerprints for a given environmental state. Machine learning with community data predicted the Baltic Sea-detected pollutants glyphosate and 2,4,6-trinitrotoluene, using the developed R package “phyloseq2ML”. Predictions by Random Forest and Artificial Neural Network were accurate. Relevant taxa were identified. The interpretability of machine learning models was found of particular importance. Microbial communities predicted even minor influencing factors in complex environments.Mikrobielle Gemeinschaften reagieren schnell und spezifisch auf sich ändernde Umgebungen und können somit bestimmte Umweltzustände anzeigen. Maschinelles Lernen mit Gemeinschaftsdaten sagte die Ostsee-präsenten Schadstoffe Glyphosat und 2,4,6-Trinitrotoluol voraus, wobei das entwickelte R-Paket "phyloseq2ML" verwendet wurde. Die Vorhersagen durch Random Forest und Artificial Neural Network waren genau. Relevante Taxa wurden identifiziert. Die Interpretierbarkeit der Modelle erwies sich als essentiell. Mikrobielle Gemeinschaften sagten selbst geringe Einflüsse in komplexen Umgebungen voraus

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Lip print based authentication in physical access control Environments

Abstract: In modern society, there is an ever-growing need to determine the identity of a person in many applications including computer security, financial transactions, borders, and forensics. Early automated methods of authentication relied mostly on possessions and knowledge. Notably these authentication methods such as passwords and access cards are based on properties that can be lost, stolen, forgotten, or disclosed. Fortunately, biometric recognition provides an elegant solution to these shortcomings by identifying a person based on their physiological or behaviourial characteristics. However, due to the diverse nature of biometric applications (e.g., unlocking a mobile phone to cross an international border), no biometric trait is likely to be ideal and satisfy the criteria for all applications. Therefore, it is necessary to investigate novel biometric modalities to establish the identity of individuals on occasions where techniques such as fingerprint or face recognition are unavailable. One such modality that has gained much attention in recent years which originates from forensic practices is the lip. This research study considers the use of computer vision methods to recognise different lip prints for achieving the task of identification. To determine whether the research problem of the study is valid, a literature review is conducted which helps identify the problem areas and the different computer vision methods that can be used for achieving lip print recognition. Accordingly, the study builds on these areas and proposes lip print identification experiments with varying models which identifies individuals solely based on their lip prints and provides guidelines for the implementation of the proposed system. Ultimately, the experiments encapsulate the broad categories of methods for achieving lip print identification. The implemented computer vision pipelines contain different stages including data augmentation, lip detection, pre-processing, feature extraction, feature representation and classification. Three pipelines were implemented from the proposed model which include a traditional machine learning pipeline, a deep learning-based pipeline and a deep hybridlearning based pipeline. Different metrics reported in literature are used to assess the performance of the prototype such as IoU, mAP, accuracy, precision, recall, F1 score, EER, ROC curve, PR curve, accuracy and loss curves. The first pipeline of the current study is a classical pipeline which employs a facial landmark detector (One Millisecond Face Alignment algorithm) to detect the lip, SURF for feature extraction, BoVW for feature representation and an SVM or K-NN classifier. The second pipeline makes use of the facial landmark detector and a VGG16 or ResNet50 architecture. The findings reveal that the ResNet50 is the best performing method for lip print identification for the current study. The third pipeline also employs the facial landmark detector, the ResNet50 architecture for feature extraction with an SVM classifier. The development of the experiments is validated and benchmarked to determine the extent or performance at which it can achieve lip print identification. The results of the benchmark for the prototype, indicate that the study accomplishes the objective of identifying individuals based on their lip prints using computer vision methods. The results also determine that the use of deep learning architectures such as ResNet50 yield promising results.M.Sc. (Science