Biblio

Currently, the Dark Web is one key platform for the online trading of illegal products and services. Analysing the .onion sites hosting marketplaces is of interest for law enforcement and security researchers. This paper presents a study on 123k listings obtained from 6 different Dark Web markets. While most of current works leverage existing datasets, these are outdated and might not contain new products, e.g., those related to the 2020 COVID pandemic. Thus, we build a custom focused crawler to collect the data. Being able to conduct analyses on current data is of considerable importance as these marketplaces continue to change and grow, both in terms of products offered and users. Also, there are several anti-crawling mechanisms being improved, making this task more difficult and, consequently, reducing the amount of data obtained in recent years on these marketplaces. We conduct a data analysis evaluating multiple characteristics regarding the products, sellers, and markets. These characteristics include, among others, the number of sales, existing categories in the markets, the origin of the products and the sellers. Our study sheds light on the products and services being offered in these markets nowadays. Moreover, we have conducted a case study on one particular productive and dynamic drug market, i.e., Cannazon. Our initial goal was to understand its evolution over time, analyzing the variation of products in stock and their price longitudinally. We realized, though, that during the period of study the market suffered a DDoS attack which damaged its reputation and affected users' trust on it, which was a potential reason which lead to the subsequent closure of the market by its operators. Consequently, our study provides insights regarding the last days of operation of such a productive market, and showcases the effectiveness of a potential intervention approach by means of disrupting the service and fostering mistrust.

Since the provision of digital services in our days (e.g. container management, transport of COVID vaccinations or LNG) in most economic sectors (e.g. maritime, health, energy) involve national, EU and non-EU stakeholders compose complex Supply Chain Services (SCS). The security of the SCS is most important and it emphasized in the NIS 2 directive [3] and it is a shared responsibility of all stakeholders involved that will need to be compliant with a scheme. In this paper we present an overview of the proposed Cybersecurity Certification Scheme for Supply Chain Services (EUSCS) as proposed by the European Commission (EC) project CYRENE [1]. The EUSCS scheme covers all the three assurance levels defined in the Cybersecurity Act (CSA) [2] taking into consideration the criticality of SCS according to the NIS 2 directive [3], the ENISA Threat Landscape for Supply Chain Attacks [4] and the CYRENE extended online Information Security Management System (ISMS) that allows all SCS stakeholders to provide and access all information needed for certification purposes making the transition from current national schemes in the EU easier.

The COVID-19 pandemic has recently emerged as a worldwide health emergency that necessitates coordinated international measures. To contain the virus's spread, governments and health organisations raced to develop vaccines that would lower Covid-19 morbidity, relieve pressure on healthcare systems, and allow economies to open. Following the COVID-19 vaccine, the vaccination certificate has been adopted to help the authorities formulate policies by controlling cross-border travelling. To address serious privacy concerns and eliminate the need for third parties to retain the trust and govern user data, in this paper, we leverage blockchain technologies in developing a secure and verifiable vaccination certificate. Our approach has the advantage of utilising a hybrid approach that implements different advanced technologies, such as the self-sovereignty concept, smart contracts and interPlanetary File System (IPFS). We rely on verifiable credentials paired with smart contracts to make decisions about who can access the system and provide on-chain verification and validation of the user and issuer DIDs. The approach was further analysed, with a focus on performance and security. Our analysis shows that our solution satisfies the security requirements for immunisation certificates.

One of the challenges of malware defense is that the attacker has the advantage over the defender. In many cases, an attack is successful and causes damage before the defender can even begin to prepare a defense. The ability to anticipate attacks and prepare defenses before they occur would be a significant scientific and technological development with practical applications in cybersecurity. In this paper, we present a method to augment machine learning-based malware detection systems by predicting signatures of future malware variants and injecting these variants into the defensive system as a vaccine. Our method uses deep learning to learn patterns of malware evolution from family histories. These evolution patterns are then used to predict future family developments. Our experiments show that a detection system augmented with these future malware signatures is able to detect future malware variants that could not be detected by the detection system alone. In particular, it detected 11 new malware variants without increasing false positives, while providing up to 5 months of lead time between prediction and attack.