This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.

The paper introduces a topology mutation – the novel concept in Moving Target Defense (MTD). MTD is a new technique that represents a significant shift in cyber defense. Traditional cybersecurity techniques have primarily focused on the passive defense of static networks only. In MTD approach cyber attackers are confused by making the attack surface dynamic, and thus harder to probe and infiltrate. The emergence of Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technology has opened up new possibilities in network architecture management. The application of combined NFV and SDN technologies provides a unique platform for implementing MTD techniques for securing the network infrastructure by morphing the logical view of the network topology.