The internet has significantly altered how we work and interact with one another.Statistics show 63.1 % of the present world population are internet users. This clearly indicates how heavily man is dependent on digital media. Digital media users are on the rise and so is the incidence of cyber crimes. People who lack experience and knowledge are more vulnerable and susceptible to phishing scams.The victims experience severe consequences as their personal credentials are at stake. Phishers use publicly available sources to acquire details about the victim's professional and personal history.Countermeasures must be implemented with the highest priority. Detection of malicious websites can significantly reduce the risk of phishing attempts.In this research, a highly accurate website phishing detection method based on URL features is proposed. We investigated eight existing machine learning classification techniques for this, including extreme gradient boosting (XGBoost), random forest (RF), adaptive boosting (AdaBoost), decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression and naïve bayes (NB) to detect malicious websites.The results show that XGboost had the best accuracy with a score of 96.71%, followed by random forest and AdaBoost.We further experimented with various hybrid combinations of the top three classifiers and observed that XGboost-Random Forest hybrid algorithms produced the best results.The hybrid model classified the websites as legitimate or phishing with an accuracy of 97.07%.

Given the modern SARS-CoV-2 coronavirus pandemic and the Universidad de Los Andes needs to protect their students against possible contagions at the university campus, we established and design a mobile application to obtain the best route between two places in the campus university for a student. The resulting path obtained by our proposed solution reduces the distance traveled by the student as well as a possible coronavirus contagion during his journey through the university campus. Therefore, we modeled two types of costs: a transport cost that models the distance to travel the campus by a student, and the contagion risk cost that models the contagion susceptibility that a student has during a displacement through the campus. As a result, we developed and validated a solution algorithm that minimizes the two modeled costs. The algorithm results were compared against a Multi-Objective mathematical optimization solution were findings show an solution approximation between the algorithm and the mathematical model. Finally, a mobile application was developed to map the optimal routes to displace between two points in the university campus given the solution algorithm.

Quality is an essential element for any company that wants to be recognized successfully; because of this, the companies undergo a certification process under a quality standard such as ISO 9001 that allows them to improve the performance of its operation, differentiate itself from its competitors, achieve a better position in the market and export quickly. In this sense, making and maintaining such certification can be under high pressure over the company, up to become a source of corruption risk, since, in emerging markets, companies may be tempted to perform unethical practices such as falsifying or adulterating documents to maintain their certifications and the benefits derived from it. Besides, considering that the quality management system audit process is based on the verification made by a third party of the documents and records of the company against the quality standard, it becomes necessary to reinforce with technology the audit process to minimize this kind of risk. Given this, a software architecture for a quality management system supported in BPMN and Blockchain technology is proposed to guarantee the integrity and immutability of the system and information, which allows exposing any attempt at fraud and facilitates the audit process's automation.