Cryptocurrency with a Conscience: Using Artificial Intelligence to Develop Money that Advances Human Ethical Values

Cryptocurrencies like Bitcoin are offering new avenues for economic empowerment to individuals around the world. However, they also provide a powerful tool that facilitates criminal activities such as human trafficking and illegal weapons sales that cause great harm to individuals and communities. Cryptocurrency advocates have argued that the ethical dimensions of cryptocurrency are not qualitatively new, insofar as money has always been understood as a passive instrument that lacks ethical values and can be used for good or ill purposes. In this paper, we challenge such a presumption that money must be ‘value-neutral.’ Building on advances in artificial intelligence, cryptography, and machine ethics, we argue that it is possible to design artificially intelligent cryptocurrencies that are not ethically neutral but which autonomously regulate their own use in a way that reflects the ethical values of particular human beings – or even entire human societies. We propose a technological framework for such cryptocurrencies and then analyse the legal, ethical, and economic implications of their use. Finally, we suggest that the development of cryptocurrencies possessing ethical as well as monetary value can provide human beings with a new economic means of positively influencing the ethos and values of their societies

Applications of Machine Learning in Cryptography: A Survey

Machine learning techniques have had a long list of applications in recent years. However, the use of machine learning in information and network security is not new. Machine learning and cryptography have many things in common. The most apparent is the processing of large amounts of data and large search spaces. In its varying techniques, machine learning has been an interesting field of study with massive potential for application. In the past three decades, machine learning techniques, whether supervised or unsupervised, have been applied in cryptographic algorithms, cryptanalysis, steganography, among other data-security-related applications. This paper presents an updated survey of applications of machine learning techniques in cryptography and cryptanalysis. The paper summarizes the research done in these areas and provides suggestions for future directions in research

Genetic boosting classification for malware detection

In the last few years virus writers have made use of new obfuscation techniques with the aim of hindering malware in order to difficult their detection by Anti-Virus engines. Strategies to reverse this trend involve executing potentially malicious programs and monitor the actions they perform in runtime, what is known as dynamic analysis. In this paper we present a method able to reach a high accuracy rate without using this kind of analysis. Instead we use a static analysis approach, which discards those samples that cannot be classified with enough certainty and need, certainly, a dynamic analysis. The K-means clustering algorithm has been used to group samples into regions according to their features. Then a boosting process, guided by a genetic algorithm, is executed in each region that are evaluated using a test dataset discarding those regions which do not reach a minimum accuracy threshold

Proceso para la identificación, clasificación y control del comportamiento de familias Ransomware

Since May 2017, where different ransomware attacks were registered worldwide that affected several companies in Europe due to the WannaCry, there has been a progressive increase between 2018 and 2019 of computer attacks that encrypt and hijack data, and then request a ransom from cyber criminals. This article contains an analysis of the different methods to detect and prevent ransomware-type malware, which mainly affects the Windows operating system. For this, it began with a characterization of the different types of ransonware, several methods were obtained for the detection and prevention of possible infections and finally families of controls were created according to the behavior of the malware, these controls allow reducing the risks of exposure, generating with this, the pertinent recommendations that can be applied in organizations. In that sense, an introduction to the concepts of malware and its life cycle is provided, in the same way, an impact measurement process is established based on the international CVSS methodology for the classification of vulnerabilities. A methodology is created that allows the classification of malware according to its damage level, medium and high impact filters were characterized, prevention and control methods were characterized, control recommendations based on the impact of different types of malware were generated, and finally the conclusions were presented.Desde mayo del 2017, en donde se registraron diferentes ataques de Ransomware a escala mundial que afectaron a varias empresas de Europa a causa del WannaCry, ha habido un aumento progresivo entre los años 2018 y 2019 de ataques informáticos que cifran y secuestran los datos, para luego solicitar un rescate por parte de los ciberdelincuentes. Este articulo contiene un análisis de los diferentes métodos para la detección y prevención de malware tipo Ransomware, que afectan principalmente al sistema operativo Windows. Para esto se inició con una caracterización de los diferentes tipos de ransonware, se obtuvieron diversos métodos para la detección y prevención de posibles infecciones y finalmente se crearon familias de controles de acuerdo con el comportamiento del malware, estos controles permiten la reducción de los riesgos de exposición, generando con ello, las recomendaciones pertinentes que pueden ser aplicadas en las organizaciones. En ese sentido, se entrega una introducción alrededor de los conceptos de malware y su ciclo de vida, así mismo, se establece un proceso de medición del impacto con base en la metodología internacional CVSS para la clasificación de las vulnerabilidades, se crea una metodología que permite la clasificación de malware de acuerdo a su nivel de daño, filtrando aquellas con impacto medio y alto, se caracterizaron los métodos de prevención y control, se generaron recomendaciones de controles con base en el impacto de los diferentes tipos de malware y finalmente se entregan las conclusiones

ML + FV = ♡\heartsuit? A Survey on the Application of Machine Learning to Formal Verification

Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on discrete mathematics and aims at ensuring correctness; ML often relies on probabilistic models and consists of learning patterns from training data. In this paper, we postulate that they are complementary in practice, and explore how ML helps FV in its classical approaches: static analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape of the current practice and catalog some of the most prominent uses of ML inside FV tools, thus offering a new perspective on FV techniques that can help researchers and practitioners to better locate the possible synergies. We discuss lessons learned from our work, point to possible improvements and offer visions for the future of the domain in the light of the science of software and systems modeling.Comment: 13 pages, no figures, 3 table

A taxonomy for threat actors' persistence techniques

[EN] The main contribution of this paper is to provide an accurate taxonomy for Persistence techniques, which allows the detection of novel techniques and the identification of appropriate countermeasures. Persistence is a key tactic for advanced offensive cyber operations. The techniques that achieve persistence have been largely analyzed in particular environments, but there is no suitable platform¿agnostic model to structure persistence techniques. This lack causes a serious problem in the modeling of activities of advanced threat actors, hindering both their detection and the implementation of countermeasures against their activities. In this paper we analyze previous work in this field and propose a novel taxonomy for persistence techniques based on persistence points, a key concept we introduce in our work as the basis for the proposed taxonomy. Our work will help analysts to identify, classify and detect compromises, significantly reducing the amount of effort needed for these tasks. It follows a logical structure that can be easy to expand and adapt, and it can be directly used in commonly accepted industry standards such as MITRE ATT&CK.Villalón-Huerta, A.; Marco-Gisbert, H.; Ripoll-Ripoll, I. (2022). A taxonomy for threat actors' persistence techniques. Computers & Security. 121:1-14. https://doi.org/10.1016/j.cose.2022.10285511412

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we will present new detection methods which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows’ memory content using a new method of Shannon Entropy calculation; methods of digital photogrammetry; the Zipf–Mandelbrot law, as well as by disassembling the memory content and analyzing the output. Finally, we present an idea and architecture of the software tool, which uses CUDA-enabled GPU hardware, to speed-up memory forensics. All three ideas are currently a work in progress. Keywords: rootkit detection, anti-forensics, memory analysis, scattered fragments, anticipatory enhancement, CUDA

Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques

Application of machine learning in the field of malware analysis is not a new concept, there have been lots of researches done on the classification of malware in android and windows environments. However, when it comes to malware analysis in the internet of things (IoT), it still requires work to be done. IoT was not designed to keeping security/privacy under consideration. Therefore, this area is full of research challenges. This study seeks to evaluate important machine learning classifiers like Support Vector Machines, Neural Network, Random Forest, Decision Trees, Naive Bayes, Bayesian Network, etc. and proposes a framework to utilize static feature extraction and selection processes highlight issues like over-fitting and generalization of classifiers to get an optimized algorithm with better performance. For background study, we used systematic literature review to find out research gaps in IoT, presented malware as a big challenge for IoT and the reasons for applying malware analysis targeting IoT devices and finally perform classification on malware dataset. The classification process used was applied on three different datasets containing file header, program header and section headers as features. Preliminary results show the accuracy of over 90% on file header, program header, and section headers. The scope of this document just discusses these results as initial results and still require some issues to be addressed which may effect on the performance measures