Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.

Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to the prediction. The current study utilized breast cancer methylation data from The Cancer Genome Atlas (TCGA), specifically the TCGA-BRCA dataset. Feature engineering techniques have been utilized to reduce data volume and make deep learning scalable. A comparative analysis of the proposed approach on Illumina 27K and 450K methylation data reveals that deep learning methodologies for cancer prediction can be coupled with feature selection models to enhance prediction accuracy. Prediction using 450K methylation markers can be accomplished in less than 13 s with an accuracy of 98.75%. Of the list of 685 genes in the feature selected 27K dataset, 578 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in five biological processes and one molecular function. Of the list of 1572 genes in the feature selected 450K data set, 1290 were mapped to Ensemble Gene IDs. This reduced set was significantly (FDR < 0.05) enriched in 95 biological processes and 17 molecular functions. Seven oncogene/tumor suppressor genes were common between the 27K and 450K feature selected gene sets. These genes were RTN4IP1, MYO18B, ANP32A, BRF1, SETBP1, NTRK1, and IGF2R. Our bioinformatics deep learning workflow, incorporating imputation and data balancing methods, is able to identify important methylation markers related to functionally important genes in breast cancer with high accuracy compared to deep learning or statistical models alone.

In this day and age, a considerable number of fatalities are caused by colon and lung cancer. Although their appearance simultaneously is rare, the likelihood of cancer cells spreading between these two organs in the absence of early diagnosis is relatively significant. The second highest cause of death worldwide is cancer. Colon and lung cancers are the most typical and lethal malignancies among the many different forms. Historically, physicians had to go through a lengthy and time-consuming process to evaluate histopathological images and identify cancer cases; however, current technology options make it possible to complete this process more quickly. This study classified the histopathological imagery LC25000 gathered at the Tampa, Florida-based James A. Haley Veterans Hospital of Lung and Colon Cancers using several Convolutional Neural Network (CNN) variants as well as the more recent vision transformer architecture. Based on the outcomes of this study, it has been observed that using the Vision Transformer provides exceptional performance outperforming deep learning CNN, VGG19, and Resnet 50 algorithms with 100% accuracy.