A single statistical framework, comprising power law distributions and scale-free networks, seems to fit a wide variety of phenomena. There is evidence that power laws appear in software at the class and function level. We show that distributions with long, fat tails in software are much more pervasive than previously established, appearing at various levels of abstraction, in diverse systems and languages. The implications of this phenomenon cover various aspects of software engineering research and practice.

Instilling good privacy practices to developers and users appears to be a difficult and daunting task. The World Wide Web encompasses a panspermia of different technologies, commercial and open source apis, evolving security standards and protocols that can be deployed towards the implementation of complex, powerful, web applications. At the same time, the proliferation of applications and services on all types of devices has also increased the attack surface for privacy threats. In this paper, we present the Privacy Flag Observatory, a platform which is one of the main tools produced by the Privacy Flag eu funded research project. The goal of this initiative is to raise awareness among European citizens of the potential privacy threats that beset the software and services they trust and use every day, including websites and smartphone applications. The Privacy Flag Observatory is one of the components that contributed to a large extent, to the success of the project’s goals. It is a real-time security and privacy threat monitoring platform whose aim is to collect, archive, analyze and present security and privacy-related information to the broader public as well as experts. Although the platform relies on crowdsourcing information gathering strategies and interacts with several other components installed on users’ devices or remote servers and databases, in this paper, we focus on the observatory platform referring only cursorily to other components such as the mobile phone add-on.

This study investigates the evolution of cybercrime in Greece by analyzing data from the Cyber Crime Division of the Hellenic Police. By combining 2023 statistics with earlier national and international data (e.g., Europol, FBI), this study presents a comprehensive 15-year view of cybercrime trends. Key findings highlight a persistent rise in cyber incidents, with financial fraud as the most common type. Other major threats include unauthorized system access, data breaches, and crimes targeting vulnerable populations. The study assesses national legislation aligned with EU directives and outlines stakeholder roles. It underscores the need for adaptive legal frameworks, inter-agency cooperation, and public awareness to mitigate Greece’s growing cybersecurity challenges.

The adoption of good security practices for the e-commerce business is a challenging problem. The enforcement of a strict legal framework is not always effective. On the other hand, simple recommendations and raising awareness actions are not sufficient to prioritize the necessity of secure e-commerce websites. Our approach is a combination of self-assessment, self-improvement and self-regulation incentivization activities, implemented in the observAtory for Security and PrIvacy DAta (ASPIDA) system. To minimize security and privacy issues in commercial websites and e-shops, we propose a hybrid, multi-dimensional methodology to gather, monitor and analyze various indicators and metrics, into an integrated system. Although several interesting efforts have been developed for monitoring compliance with basic cybersecurity principles, their implementation is limited without any stimulus for the participants. ASPIDA automatically analyzes a set of technical security and privacy characteristics of commercial websites and correlates the output with the results of questionnaires answered by the respective owners, web-developers and webadministrators. Positive evaluation in the automatic testing and participation in the e-commerce cybersecurity survey generates a digital badge of good practices for the specific website. The digital badge is a premium for informed e-buyers that are aware of the impact of recent catastrophic security incidents and the importance of personal data.

Internet of Things networks evolve as a rapidly growing field for security threats, such as Denial-of-Service cross-layer attacks, due to their heterogeneous and resource-constrained environment. Intrusion detection systems (IDSs) serve as a vital defense mechanism in modern cybersecurity. However, the adoption of such a system, especially one that adopts a cross-layer strategy, requires a standardized, multifaceted evaluation framework that accounts for both detection capability and operational overhead. To address these challenges, we proposed a modular weight-based framework that evaluates cross-layer Machine Learning (ML) IDS across multiple dimensions, namely, detection effectiveness and generalizability, data quality, and attack coverage and practical deployability. We then applied this framework to the state-of-the-art cross-layer ML IDSs identified through the PRISMA framework. This proof-of-concept application illustrates how current evaluation practices generate disparate, fragmented results, while also highlighting the limitations inherent in retrospective literature-based scoring.