Experimental Study based on the Implementation of a Regulatory Framework for the Improvement of Cyber Resilience in SMEs

Currently, applying regulations oriented to cybersecurity, cyber resilience is relevant to face the high rates of cyberattacks, which have caused an interruption in the operational processes of organizations, generating an economic loss, and affecting the continuity of their business processes on the web. In this scenario, small and medium-sized enterprises (SMEs) are the most affected due to their weak technological infrastructure. Given this, this experimental study was developed to implement a regulatory framework for the improvement of cyber resilience; the criteria anticipate, resist, recover and evolve presented significant statistical values of improvement after the application of the experiment. This research contributes to counteract the refusal to use information technologies for business development; Improvement actions were carried out to face threats and computer vulnerabilities to which organizations are exposed when carrying out operations in cyberspace

A Comprehensive Systematic Review of Neural Networks and Their Impact on the Detection of Malicious Websites in Network Users

The large branches of Machine Learning represent an immense support for the detection of malicious websites, they can predict whether a URL is malicious or benign, leaving aside the cyber attacks that can generate for network users who are unaware of them. The objective of the research was to know the state of the art about Neural Networks and their impact for the Detection of malicious Websites in network users. For this purpose, a systematic literature review (SLR) was conducted from 2017 to 2021. The search identified 561 963 papers from different sources such as Taylor & Francis Online, IEEE Xplore, ARDI, ScienceDirect, Wiley Online Library, ACM Digital Library and Microsoft Academic. Of the papers only 82 were considered based on exclusion criteria formulated by the author. As a result of the SLR, studies focused on machine learning (ML), where it recommends the use of algorithms to have a better and efficient prediction of malicious websites. For the researchers, this review presents a mapping of the findings on the most used machine learning techniques for malicious website detection, which are essential for a study because they increase the accuracy of an algorithm. It also shows the main machine learning methodologies that are used in the research papers