Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.

The traditional technologies, tools and procedures of any network cannot be protected from attackers due to the unchanged services and configurations of the networks. To get rid of the asymmetrical feature, Moving Target Defense technique constantly changes the platform conformation which reduces success ratio of the cyberattack. Users are faced with realness with the increase of continual, progressive, and smart attacks. However, the defenders often follow the attackers in taking suitable action to frustrate expected attackers. The moving target defense idea appeared as a preemptive protect mechanism aimed at preventing attacks. This paper conducts a comprehensive study to cover the following aspects of moving target defense, characteristics of target attacks and its limitation, classifications of defense types, major methodologies, promising defense solutions, assessment methods and applications of defense. Finally, we conclude the study and the future concern proposals. The purpose of the study is to give general directions of research regarding critical features of defense techniques to scholars seeking to improve proactive and adaptive moving target defense mechanisms.