New drug discovery is a time-consuming and costly process. Several drugs have been in clinical trials for a very long period. Finding a new target for existing medications can be an effective strategy to reduce the lengthy and costly drug development cycle. Drug repurposing (or repositioning) is a cost-effective approach or finding drugs that can treat diseases for which thosemedications are not currently prescribed. Drug repurposing to treat both common and rare diseases is becoming an attractive option because it involves using already approved drugs. Through drug repurposing, we can identify promising drugs for further clinical investigations. This paper presents machine learning techniques for drug repurposing to find existing drugs as an alternate medication for other diseases through drug-drug, drug-genes, drug-enzymes, and drug-targets interactions. We develop a model to find similar drugs that can treat similar diseases. We then use the model to predict potential candidate drugs for rare orphan diseases.

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Due to the rise in the Internet of Health Things (IoHT), cyber-attacks, particularly data intrusions, have become an issue for security experts. In this work, we analyze the performance of traditional statistical, machine learning, and graph-based anomaly detection approaches in response to this problem. We believe that understanding intrusion patterns can aid in the prevention of future attacks. In this work, we use the ARMA model for statistical analysis. We also use several machine learning approaches such as multinomial naive bayes, ran- dom forest, neural networks, XGBClassifier, and support vector machines (SVM). However, while our experiments show that machine learning (ML) techniques have higher precision, accuracy, and F1 score than graph-based techniques, there are aspects to a graph-based approach that could aid security experts in the discovery of certain data breaches by combining the graph-based with the statistical and ML methods. Experiments also show combining different anomaly detection techniques allows for a diverse set of intrusion patterns to be discovered. By recognizing the power of both machine learning and graph-based approaches, we analyze their precision and accuracy while explaining how existing state-of-the-art methods can detect breach patterns. Finally, by identifying the characteristics of breach patterns, we present information that security experts can use to prevent future data intrusions.