Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. The main contribution of this work is a dataset coming from a real-world, academic network. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The dataset contains 44 network features and an unbalanced distribution of classes. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project.

E-learning platforms are valuable tools to support and enhance the learning and teaching process during online classes. Even though these platforms are already well-known and have been widely used for a long time, transitioning to fully online activities with a large number of users can lead to an exponential growth in resource usage. Moreover, the availability of these platforms becomes critical since the learning activities cannot be interrupted, and the vital information (assignments, grades, and shared resources) cannot be lost. Thus, the system administrators must implement a scalable solution to accommodate many users and activities. Determining the most suitable architecture is a non-trivial task that must consider the various tools, services, and interconnect options that are available. This paper presents our approach to setting up an integrated high-workload e-learning setup used for online classes in University Politehnica of Bucharest and can be used as a reference by other universities.

The rapid development of an increasing volume of web apps and the improper testing of the resulting code invariably provide more attack surfaces to potentially exploit. This leads to higher chances of facing cybersecurity breaches that can negatively impact both the users and providers of web services. Moreover, current data leaks resulting from breaches are most probably the fuel of future breaches and social engineering attacks. Given the context, a better analysis and understanding of web attacks are of the utmost priority. Our study provides practical insights into developing, implementing, deploying, and actively monitoring a web application-agnostic honeypot with the objective of improving the odds of defending against web attacks.

IT help desks at large organizations face a high volume of recurrent, well-documented user requests that nevertheless require human-written replies, creating a persistent staff workload that is repetitive in content but non-trivial in tone and procedural correctness. We present FroLineR, short for Front-Line Response, a system that drafts the initial staff reply to such tickets in the login and account-activation category and integrates into a human-in-the-loop ticketing workflow on a Romanian-language ticketing platform. The generator is an unmodified instruct model augmented with retrieval from a small set of hand-curated guide documents, using a Romanian system prompt refined over several rounds of staff review. To evaluate and refine the prompt without manual labeling, we cluster the first user message of every historical thread with both BERTopic and Semantic Signal Separation ($S^3$), score configurations along coherence and lexical-diversity axes, and extract a 200-message evaluation set from the winning model. Prompt convergence was certified by several rounds of manual review by support staff. The production system is quantized to Q4_K_M GGUF, served through llama-cpp-python behind a small Flask API, and deployed with GPU offloading on the target server, reducing end-to-end per-answer latency from approximately 830 s on the server’s CPU to roughly 61 s once layers are offloaded to the GPU, with no observable degradation in answer quality.

Recent improvements in hardware and software have enabled a paradigm shift in satellite computing, moving from purpose-built satellites running a single application to platforms capable of executing and even receiving new workloads on orbit. This evolution has allowed image processing to migrate from ground stations to single- or multi-node satellite clusters, with only processed results transmitted, significantly reducing end-to-end latency. This paper proposes ORCHIDE, an orchestration solution built on cloud-native technologies such as Kubernetes and Argo, purpose built for space edge computing. A key capability of ORCHIDE is its support for unikernels—minimal, single-application virtual machines—alongside containers. Compared to traditional containerized deployments, unikernels substantially reduce CPU and memory footprint, achieve short boot times, and produce smaller binary images. ORCHIDE further enables unikernel workloads to leverage heterogeneous accelerator hardware, including FPGAs, through a dedicated accelerator management library. We describe the system architecture, the scheduling model, and the minimum target hardware required for deployment. Three clusters of varying topology were used to evaluate ORCHIDE, demonstrating that it operates effectively on both single- and multi-node heterogeneous configurations. Preliminary results show the ORCHIDE platform being able to run in heterogeneous and single-node environments with as low as 4 cores and 8 GB of memory, offering potential users the flexibility to compose satellite hardware to best match their mission requirements.