The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset.

In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.