The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

Cyber crimes rapidly increased over the past years, with attackers performing large-scale activities, using sophisticated and complex tactics and techniques, that have targeted governments, companies, and even strategic infrastructures. To tackle these attacks, the cyber-security community usually shares Cyber Threat Intelligence (CTI) that includes the collected Indicators of Compromise (IoC) using several open or private sharing platforms. In this paper, we study the informativeness and relevance of the IoCs related to cyber crimes following a major real-world event such as the war in Ukraine, which started in February 2022. To this end, we analyze different kinds of attacks available in a crowd-sourced dataset of Cyber Threat Intelligence (CTI) reports. Our analysis shows that while this data is able to capture major trends such as the ones following major events, the degree of miscellaneous information inside the reports makes it difficult to discern the association of a specific trace unequivocally.

Behavioural analysis based on filesystem operations is one of the most promising approaches for the detection of ransomware. Nonetheless, tracking all the operations on all the files for all the processes can introduce a significant overhead on the monitored system. We present MUSTARD, a solution to dynamically adapt the degree of monitoring for each process based on their behaviour to achieve a reduction of monitoring resources for the benign processes.

Targeted online advertising is a multi-billion dollar business based on the ability of profiling and delivering targeted ads to a wide range of users. Due to the privacy erosion associated with such business, researchers are trying to understand how profiling works and anti-tracking applications are becoming popular among users. Both research and privacy-enhancing apps, however, target ad-networks or over-the-top providers that have unrestricted access to users' online activity. There seems to be little interest in potential profiling activities by "network observers" like ISPs or VPN providers. On the one side, this may be explained by the pervasiveness of TLS that secures connections end-to-end. On the other side, TLS does leak some information, and it is not clear what an eavesdropper can learn about a user, despite her traffic being encrypted.

The increase in network traffic volumes challenges the scalability of security analysis tools. In this paper, we present NetLearn, a solution to identify potentially malicious network entities from large amounts of network traffic data. NetLearn applies recently developed natural language processing algorithms to discover security-relevant relationships between the observed network entities, e.g., domain names and IP addresses, without requiring external sources of information for its analysis.

Nowadays Internet companies that offer valuable services "for free" are becoming ubiquitous. Users benefiting from these services have to expose their personal information through these services as they utilize them. On the other hand, personal information is becoming a merchandisable commodity, venues that sell personal information by auction are emerging. One of these markets is in the form of advertising systems. Despite being a lucrative business, the hoarding of user personal information by commercial companies is a growing issue primarily because of its non-transparent nature. In this paper we present a data valuation portal that shades light on what kinds of personal information is on market and the financial value of it.

Ad networks use the behaviors of online users to associate them with preferences (features), and market these features to enable advertisers to target online users. Typical features associated with users include location, interests, gender, age, and etc. Furthemore, ad networks provide their clients with campaing creation tools to help to them to configure and run campains. In this paper, we study the pricing of ads using the ad campaing planning tools of ad networks. We develop tools to collect the suggested bid prices from two platforms: YouTube and Facebook. Analyzing these prices we find that United States is the most expensive country in both platforms. We also find that the most expensive preferences are different in YouTube and Facebook. In YouTube, the top preferences are related to Oil & Gas, while in Facebook are devices, ethnics or politics depending on the type of bidding. Finally, we do not find any price difference genders in Facebook.

While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators.

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question of whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter. This paper presents a detailed longitudinal characterization of G+ based on large-scale measurements. We identify the main components of G+ structure and characterize the key feature of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics to other major online social networks (OSNs). We show that despite the dramatic growth in the size of G+, the relative size of the LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity, and an even smaller fraction of these users attracts any reaction. The identity of users with most followers and reactions reveal that most of them are related to high-tech industry. To our knowledge, this study offers the most comprehensive characterization of G+ based on the largest collected datasets.

We study the problem of reducing power consumption in an Internet Service Provider (ISP) network by designing the content distribution infrastructure managed by the operator. We propose an algorithm to optimally decide where to cache the content inside the ISP network. We evaluate our solution over two case studies driven by operators feedback. Results show that the energy-efficient design of the content infrastructure brings substantial savings, both in terms of energy and in terms of bandwidth required at the peering point of the operator. Moreover, we study the impact of the content characteristics and the power consumption models. Finally, we derive some insights for the design of future energy-aware networks.

During recent years, a few countries have put in place online antipiracy laws and there has been some major enforcement actions against violators. This raises the question that to what extent antipiracy actions have been effective in deterring online piracy? This is a challenging issue to explore because of the difficulty to capture user behavior, and to identify the subtle effect of various underlying (and potentially opposing) causes. In this paper, we tackle this question by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law, on publishers in the largest BitTorrent portal who are major providers of copyrighted content online. We capture snapshots of BitTorrent publishers at proper times relative to the targeted antipiracy event and use the trends in the number and the level of activity of these publishers to assess their reaction to these events. Our investigation illustrates the importance of examining the impact of antipiracy events on different groups of publishers and provides valuable insights on the effect of selected major antipiracy actions on publishers' behavior.

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?".