The applications that use blockchain are cryptocurrencies, decentralized finance applications, video games and many others. Most of these applications trust that the blockchain will prevent issues like fraud, thanks to the built-in cryptographic mechanisms provided by the data structure and the consensus protocol. However, blockchains suffers from what is called a 51% attack or majority attack, which is considered a high risk for the integrity of these blockchains, where if a miner, or a group of them, has more than half the computing capability of the network, it can rewrite the blockchain. Even though this attack is possible in theory, it is regarded as hard-achievable in practice, due to the assumption that, with enough active members, it is very complicated to have that much computing power; however, this assumption has not been studied with enough detail. In this work, a detailed characterization of the miners in the Bitcoin and Crypto Ethereum blockchains is presented, with the aim of proving the computing distribution assumption and to creating profiles that may allow the detection of anomalous behaviors and prevent 51% attacks. The results of the analysis show that, in the last years, there has been an increasing concentration of hash rate power in a very small set of miners, which generates a real risk for current blockchains. Also, that there is a pattern in mining among the main miners, which makes it possible to identify out-of-normal behavior.

The paper shows an informatics security diagnosis' results, applied to a private organization in the department of Boyacá, Colombia. It developed and implemented a vulnerability plan management, tailored according to that organization's needs. It began by raising the company's technological inventory, in order to identify the real problems that can cause any information security vulnerabilities. This research showed the plan's effectiveness, within the company, achieved after a 6-month monitoring period. It reached 70% vulnerabilities' reduction, by applying some remedies previously designed. Furthermore, the paper shows several comparative informatics tools' tables, which were selected and used in the management plan's application stage that could be a help for a future research, in the tools selection for monitoring and vulnerability's management.

Cryptojacking or illegal mining is a form of malware that hides in the victim’s computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim’s computer. This attack has increased due to the rise of cryptocurrencies and their profitability and its difficult detection by the user. The identification and blocking of this type of malware have become an aspect of research related to cryptocurrencies and blockchain technology; in the literature, some machine learning and deep learning techniques are presented, but they are still susceptible to improvement. In this work, we explore multiple Machine Learning classification models for detecting cryptojacking on websites, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost. To this end, we make use of a dataset, composed of network and host features’ samples, to which we apply various feature selection methods such as those based on statistical methods, e.g., Test Anova, and other methods as Wrappers, not only to reduce the complexity of the built models but also to discover the features with the greatest predictive power. Our results suggest that simple models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor models, can achieve success rate similar to or greater than that of advanced algorithms such as XGBoost and even those of other works based on Deep Learning.

Consensus protocols are a fundamental part of any blockchain; although several protocols have been in operation for several years, they still have drawbacks. For instance, some may be susceptible to a 51% attack, also known as a majority attack, which may suppose a high risk to the trustworthiness of the blockchains. Although this attack is theoretically possible, executing it in practice is often regarded as arduous because of the premise that, with sufficiently active members, it is not ’straightforward’ to have much computing power. Since it represents a possible vulnerability, the community has made efforts to solve this and other blockchain problems, which has resulted in the birth of alternative consensus protocols, e.g., the proof of accuracy protocol. This paper presents a detailed proposal of a proof-of-accuracy protocol. It aims to democratize the miners’ participation within a blockchain, control the miners’ computing power, and mitigate the majority attacks.