In this paper, a literature review of recent advances in machine learning approaches for cyber defense is presented. Relevant articles in the databases ACM DL, IEEE Xplore DL, and ScienceDirect were identified and supplemented by forward and backward searches. In total, 70 articles were identified to meet the scope of the literature review. The following article gives an overview of classifications, datasets, and algorithms of machine learning in cyber defense. Limitations and future research areas are identified.

The consequences of alcohol use disorders affect around 17 million people in the United States. To prevent healthy people from developing an alcohol use disorder, early detection is achieved using different screening methods. However, these methods are mostly based on self-tests, which can be easily influenced by the subject. In order to prevent healthy people from developing an alcohol use disorder through their alcohol consumption, drinking behavior, and alcohol-related problems, we propose a novel machine learning approach. With this approach it is possible to classify healthy people with an accuracy of 69 percent based on EEG recordings in assessing the danger of developing an alcohol use disorder or not. To obtain this result, the frequency range of the EEG data used was divided into 99 fine bands. Using a machine learning algorithm, the five most important bands were identified.