# Machine Learning: Models, Challenges, and Research Directions
###### Abstract  
Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains. artificial intelligence; data pre-processing; machine learning; supervised learning; semi-supervised learning; optimization techniques; reinforcement learning; unsupervised learning +
Footnote †: journal: _Future Internet_  
of labeled samples and a high number of unlabeled samples [4; 5], while reinforcement learning models are trained based on rewarding the desired actions or punishing undesired behaviors. In such models, the agent attempts to interpret the environment, act, and learn via trial and error [6; 7; 8]. The subsequent step is to ensure that the trained model achieves optimality. ML models involve many parameters. These parameters have to be optimized using hyperparameter tuning techniques [9; 10; 11; 12; 13; 14; 15]. A hyperparameter tuning technique is an algorithm that finds the best parameters of an ML model with the best performance. Examples of these tuning techniques include genetic algorithms, random search, and grid search [13]. For instance, in the work described in [14], the authors used a tuning technique, a tree-structured parzen estimator, to optimize the parameters of several boosting-based models, such as adaptive boosting, categorical boosting, and gradient boosting. Examples of the parameters that have to be optimized are the estimator number and learning. The number of estimators indicates the number of the trees in the forest, whereas the learning rate shows how fast the model learns. The number of the estimators and the learning rate were set to 100 and 0.29, respectively, after applying the tree-structured parzen estimator tuning technique. The simulation results showed exponential improvements in the performance of these selected ML models as a result of optimizing their parameters using a hyperparameter tuning technique. Another study [16] used a grid search to improve the performance of several traditional and ensemble ML models, namely K nearest neighbor, naive bayes, random forest, and stacking in detecting intrusions on the smart grid. Examples of the hyperparameters are the number of neighbors, weight, and size of the leaves used in the K nearest neighbor model. The number of neighbors refers to the number of the nearest neighbors in the voting procedure which occurs in K nearest neighbor model, while the weight in this model refers to the given weights to the nearest K point using the kernel function. The size of the leaves manages the minimum number of points in a node and effectively adjusts the tradeoff between the cost of node traversal and the cost of a brute-force distance estimate.