Introduction to Machine Learning with Applications in Information Security

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

Tree data analysis has many applications in information security. In particular, HTML pages' DOM trees are an important target of analysis because web pages can be vectors for, and targets of, major cyberattacks like phishing. Previous attempts to incorporate tree data analysis into security applications, however, have been hampered by the lack of efficient methods for tree data analysis in machine learning. As such, most security research has focused on data representable as vectors of real numbers, like most machine learning work. Recent work, however, has yielded several efficiency break-throughs in tree analysis. One example is kernel methods, a methodological bridge that fills the gap between discretely-structured data (like trees) and multivariate analysis. Kernel methods enable applying a variety of multivariate analysis techniques such as SVM and PCA to trees. The method we are interested in is the subpath kernel. The subpath kernel offers the following advantages: (1) it is invariant over ordered and unordered trees; (2) it can be computed using an extremely fast linear-time algorithm compared to the quadratic time required to compute values of most tree kernels; (3) its excellent prediction accuracy has been proven through intensive experiments. This paper proposes a subpath kernel-based method for tree-structured security data. To demonstrate the effectiveness of our method, we apply it to the problem of detecting fake e-commerce sites, a sub-problem of phishing detection with a significant real-world financial cost. In an experiment on a real dataset of fake sites provided by a major e-commerce company, our method exhibited accuracy as high as 0.998 when training SVM with as few as 1,000 instances. Its generalization efficiency is also excellent: with only 100 training instances, the accuracy score reaches 0.996. While previous phishing detection methods relied on textual content, URL components, and blacklists, our approach is the first to leverage DOM trees, which makes it both more effective and more robust against adversarial attacks. Unlike URL or content changes, changing a page's DOM structure incurs large costs to criminals.

Purpose of the article: Analysis of intellectualized weapons using machine learning from the point of view of information security. Development of proposals for the deployment of work in the field of information security in similar products. Research method: System analysis of machine learning systems as objects of protection. Determination on the basis of the analysis of rational priority directions for improving these systems in terms of ensuring information security. Obtained result: New threats to information security arising from the use of weapons and military equipment with elements of artificial intelligence are presented. Machine learning systems are considered by the authors as an object of protection, which made it possible to determine the protected assets of such systems, their vulnerabilities, threats and possible attacks on them. The article analyzes the measures to neutralize the identified threats based on the taxonomy proposed by the US National Institute of Standards and Technology. The insufficiency of the existing regulatory methodological framework in the field of information protection to ensure the security of machine learning systems has been determined. An approach is proposed that should be used in the development and security assessment of systems using machine learning. Proposals for the deployment of work in the field of ensuring the security of intelligent weapons using machine learning technologies are presented.

The evolution of communication networks and information systems, to support wireless access, cloud and grid computing, and big data, provides great business opportunities. However, it also generates a new trend of sophisticated network threats and offers several challenges in securing information and systems confidentiality, integrity and availability. The traditional techniques used by security experts are mostly static and lack the much needed characteristics of adaptation and self-organization, computational efficiency and error resilience to deal with evolving attacks. The inherent characteristics of computational intelligence (CI) paradigms provide a promising alternative that has gained popularity resulting in significant applications in information security. There is a plethora of CI paradigms commonly used in this domain including artificial neural networks, evolutionary computing, fuzzy systems, and swarm intelligence. This chapter provides an overview of the widely-recognized CI paradigms and shades the light on some of their potential applications in information security.

The evolution of communication networks and information systems, to support wireless access, cloud and grid computing, and big data, provides great business opportunities. However, it also generates a new trend of sophisticated network threats and offers several challenges in securing information and systems confidentiality, integrity and availability. The traditional techniques used by security experts are mostly static and lack the much needed characteristics of adaptation and self-organization, computational efficiency and error resilience to deal with evolving attacks. The inherent characteristics of computational intelligence (CI) paradigms provide a promising alternative that has gained popularity resulting in significant applications in information security. There is a plethora of CI paradigms commonly used in this domain including artificial neural networks, evolutionary computing, fuzzy systems, and swarm intelligence. This chapter provides an overview of the widely-recognized CI paradigms and shades the light on some of their potential applications in information security.

PurposeNoninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application of machine learning (ML) based peritumoral ultrasound radiomics signature (PURS), compared with intratumoral radiomics (IURS) and clinicopathologic factors, for early prediction of pCR.MethodsWe analyzed 358 locally advanced breast cancer patients (250 in the training set and 108 in the test set), who accepted NAC and post NAC surgery at our institution. The clinical and pathological data were analyzed using the independent t test and the Chi-square test to determine the factors associated with pCR. The PURS and IURS of baseline breast tumors were extracted by using 3D-slicer and PyRadiomics software. Five ML classifiers including linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and adaptive boosting (AdaBoost) were applied to construct radiomics predictive models. The performance of PURS, IURS models and clinicopathologic predictors were assessed with respect to sensitivity, specificity, accuracy and the areas under the curve (AUCs).ResultsNinety-seven patients achieved pCR. The clinicopathologic predictors obtained an AUC of 0.759. Among PURS models, the RF classifier achieved better efficacy (AUC of 0.889) than LR (0.849), AdaBoost (0.823), SVM (0.746) and LDA (0.732). The RF classifier also obtained a maximum AUC of 0.931 than 0.920 (AdaBoost), 0.875 (LR), 0.825 (SVM), and 0.798 (LDA) in IURS models in the test set. The RF based PURS yielded higher predictive ability (AUC 0.889; 95% CI 0.814, 0.947) than clinicopathologic factors (AUC 0.759; 95% CI 0.657, 0.861; p < 0.05), but lower efficacy compared with IURS (AUC 0.931; 95% CI 0.865, 0.980; p < 0.05).ConclusionThe peritumoral US radiomics, as a novel potential biomarker, can assist clinical therapy decisions.

Exploring sex classification from earprints - A comparison of supervised machine learning algorithms and conventional linear discriminant analysis.

Objective: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Approach: Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. Main results: HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Significance: Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

This paper introduces both Machine Learning (ML) and state-of-the-art Deep Learning (DL) methods for Named Entity Recognition (NER) in Urdu a low-resource language. The work compares a variety of models such as Conditional Random Fields (CRF), Logistic Regression, Support Vector Machines (SVM), BiLSTM+GRU, mBERT, and XLM-RoBERTa on a cross domain dataset of more than 1 million tokens for eight entity classes. Performance was compared using typical metrics: precision, recall, F1-score, and accuracy. Among the ML models, CRF had the best F1-score of 0.9899 and accuracy of 97%, lagging behind Logistic Regression and SVM. However, deep learning models performed much better than traditional approaches. The results show that our proposed hybrid technique outperforms existing state of the art techniques on Urdu NER, achieving an F-score of up to 0.997 when using BiLSTM+GRU, followed closely by XLM-RoBERTa and mBERT with F1-scores of 0.9969 and 0.996, respectively. One of the novel contributions of this paper is training and testing models on naturally ordered, domain-specific Urdu text, and building an in-house annotated corpus. It is proven from our results that transformer-based and hybrid recurrent models perform incredibly well for under-resourced NER tasks given the provision of clean, domain-specific data. This paper opens the way to future work on building real-world NLP applications for under-resourced languages.

In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.

An urgent problem in the field of information security – the provision of human resources in the field of information security in the Russian Federation is presented. One of the factors influencing this is the lack of the possibility of determining at the initial stage the optimal choice of the future direction of activity of graduates of higher educational institutions with specialization in this field. To solve this problem, it is proposed to use methods of data mining, machine learning and algorithms based on artificial neural networks. For the successful application of data mining methods, several factors should be taken into account: the data set must be large enough. Secondly; the data must be structured and analyzed to extract the most important information; the data analysis methods must be adequate and accurate in order to determine the purpose of the graduate with the highest accuracy. Taking into account these factors, the article suggests a way to form the initial data set and prepare it using statistical methods to predict the success of the professional activity of an information security specialist. The results of this work can be useful for higher educational institutions, specialists in the field of information security, heads of personnel departments who are interested in optimizing the process of selecting qualified employees in this field.

The overabundance of data which now characterizes the work environment of meteorologists, data scientists and risk managers requires new methods of data treatment. Thus, Machine Learning entered into weather modelisation. Because they have chosen to work with non-linear models rather than linear ones, weather specialists are presented as quick to adopt new techniques, more suited to the realities they are trying to capture. This even made some say that weathermen had much to teach to economists, who fail to abandon their old econometric ‘recipes’. Does Machine learning really solve all the problems at once? Are Linear Models really outdated?

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

Medical imaging is becoming indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multislice CT, 3D ultrasound imaging, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), positron-emission tomography (PET)/CT, electrical impedance tomography, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Because of large variations and complexityit is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require “learning from examples” for accurate representation of data and prior knowledge. Because of its essential needs, machine learning in medical imaging is one of the most promising, growing fields. The main aim of this special issue is to help advance the scientific research within the broad field of machine learning in medical imaging. The special issue was planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010) [1], which was the first workshop on this topic, held at the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010) in September, 2010, in Beijing, China. This special issue is one in a series of special issues of journals on this topic [2]; it focuses on major trends and challenges in this area, and it presents work aimed at identifying new cutting-edge techniques and their use in medical imaging. The quality level of the submissions for this special issue was very high. A total of 17 papers were submitted to this issue in response to the call for papers. Based on a rigorous review process, 10 papers (59%) were accepted for publication in the special issue. The special issue starts by a review of studies on a class of machine-learning techniques, called pixel/voxel-based machine learning, in medical imaging by K. Suzuki. A series of medical imaging applications of machine-learning techniques are presented. A large variety of applications are well represented here, including organ modeling by D. Wang et al. and X. Qiao and Y.-W. Chen, brain function estimation by V. Michel et al., image reconstruction by H. Shouno et al., lesion classification by P. Wighton et al., modality classification by X.-H. Han and Y.-W. Chen, lesion segmentation by M. Zortea et al., organ segmentation by S. Alzubi et al., and visualization of molecular signals by F. Mattoli et al. Also, the issue covers various biomedical imaging modalities, including MRI by D. Wang et al., CT by X. Qiao and Y.-W. Chen and H. Shouno et al., functional MRI by V. Michel et al., dermoscopy by P. Wighton et al. and M. Zortea et al., scintigraphy by X.-H. Han and Y.-W. Chen, ultrasound imaging by X.-H. Han and Y.-W. Chen, radiography by X.-H. Han and Y.-W. Chen, MR angiography by S. Alzubi et al., and microscopy by F. Mattoli et al. as well as a variety of organs, including the kidneys by D. Wang et al., liver by X. Qiao and Y.-W. Chen, brain by V. Michel et al. and F. Mattoli et al., chest by S. Alzubi et al., skin by P. Wighton et al. and M. Zortea et al., and heart by F. Mattoli et al. Various machine-learning techniques were developed/used to solve the respective problems, including structured dictionary learning by D. Wang et al., generalized N-dimensional principal component analysis by X. Qiao et al., multiclass sparse Bayesian regression by V. Michel et al., Bayesian hyperparameter inference by H. Shouno et al., supervised learning of probabilistic models based on maximum aposteriori estimation and conditional random fields by P. Wighton et al., joint kernel equal contribution in support vector classification by X.-H. Han and Y.-W. Chen, and iterative hybrid classification by M. Zortea et al. We are grateful to all authors for their excellent contributions to this special issue and to all reviewers for their reviews and constructive suggestions. We hope that this special issue will inspire further ideas for creative research, advance the field of machine learning in medical imaging, and facilitate the translation of the research from bench to bedside. Kenji Suzuki Pingkun Yan Fei Wang Dinggang Shen