The Internet of Things rapid growth poses privacy and security challenges for the traditional key storage methods. Physical Unclonable Functions offer a potential solution but require secure fuzzy extractors to ensure reliable replication. This paper introduces X-Lock, a novel and secure computational fuzzy extractor that addresses the limitations faced by traditional solutions in resource-constrained IoT devices. X-Lock offers a reusable and robust solution, effectively mitigating the impacts of bias and correlation through its design. Leveraging the preferred state of a noisy source, X-Lock encrypts a random string of bits that can be later used as seed to generate multiple secret keys. To prove our claims, we provide a comprehensive theoretical analysis, addressing security considerations, and implement the proposed model. To evaluate the effectiveness and superiority of our proposal, we also provide practical experiments and compare the results with existing approaches. The experimental findings demonstrate the efficacy of our algorithm, showing comparable memory cost (≈2.4 KB for storing 5 keys of 128 bits) while being 3 orders of magnitude faster with respect to the state-of-the-art solution (0.086 ms against 15.51 s).

Mergers and acquisitions are pivotal strategies employed by companies to maintain competitiveness, leading to enhanced production efficiency, scale, and market dominance. Due to their significant financial implications, predicting these operations has become a profitable area of study for both scholars and industry professionals. The accurate forecasting of mergers and acquisitions activities is a complex task, demanding advanced statistical tools and generating substantial returns for stakeholders and investors. Existing research in this field has proposed various methods encompassing econometric models, machine learning algorithms, and sentiment analysis. However, the effectiveness and accuracy of these approaches vary considerably, posing challenges for the development of robust and scalable models. In this paper, we present a novel approach to forecast mergers and acquisitions activities by utilizing social network analysis. By examining temporal changes in social network graphs of the involved entities, potential transactions can be identified prior to public announcements, granting a significant advantage in the forecasting process. To validate our approach, we conduct a case study on three recent acquisitions made by Microsoft, leveraging the social network platform Twitter. Our methodology involves distinguishing employees from random users and subsequently analyzing the evolution of mutual connections over time. The results demonstrate a strong link between engaged firms, with the connections between Microsoft employees and acquired companies ranging from five to twenty times higher than those of baseline companies in the two years preceding the official announcement. These findings underscore the potential of social network analysis in accurately forecasting mergers and acquisitions activities and open avenues for the development of innovative methodologies.

People usually think that digital screens are reliable devices. Unfortunately, attackers can exploit this blind trust to persuade a user to perform unintended actions. In this paper, we present a novel type of Man-in-the-Middle attack named Man-in-the-Video. Man-in-the-Video intercepts the video stream flowing between a computer and its screen and modifies it on-the-fly. The objective of such attack is to distort the perception of reality and to induce improper user behaviour. We implemented HackDMI, a Man-in-the-Video attack performed over an HDMI cable. We applied this attack to a realistic threat scenario (i.e., phishing) and we evaluated it with quantitative measures. HackDMI is able to deceptively modify a 720p video stream, while maintaining a frame-rate of 14FPS. We also recorded three demo videos for qualitative evaluation.

Collective Remote Attestation (CRA) is a well-established approach where a single Verifier attests the integrity of multiple devices in a single execution of the challenge-response protocol. Current CRA solutions are well-suited for Internet of Things (IoT) networks, where the devices are distributed in a mesh topology and communicate only with their physical neighbours. Recent advancements on low-energy protocols, though, enabled the IoT devices to connected to the Internet, thus disrupting the concept of physical neighbour. In this paper, we propose HolA (Holistic and Autonomous Attestation), the first CRA scheme designed for Internet-like IoT networks. HolA provides defence against attacks targeting both the nodes and the network infrastructure. We deployed HolA on both a network of real devices (i.e., 5 Raspberry Pis) and a simulated environment (i.e., 1M devices in an Omnet++ network). Our results demonstrate that HolA can resist against a disruptive attacker that compromises up to half of the network devices and that tampers with network traffic. HolA can verify the integrity of 1M devices in around 12 s while the state-of-the-art requires 71 s. Finally, HolA requires 7 times less memory per device compared with the state-of-the-art.